Artificial Intelligence perfectly captures the zeitgeist of today’s technology. With each day progressing, we are discovering new use cases for AI. Use cases that can improve our quality of life, and also a few that threaten it if development isn’t kept in check.
Businesses from banking to healthcare are implementing AI in their operations. We’ve reached a stage wherein an AI strategy is a must-have for businesses, and a lack thereof can prove to be a serious disadvantage.
AI is here to stay, and it is revolutionizing businesses, with an impact quite similar or perhaps stronger than that of the industrial revolution.
Here are some of the latest innovations in the field:
Advanced autonomous vehicles
Autonomous vehicle manufacturers are figuring out ways to tackle complex computer vision problems. A common problem in this field has been the lack of access to edge-case training data. For example, most autonomous vehicles are only trained to identify pedestrians, road markings, other cars, and signals. But, how should such a vehicle react to a group of 4th of July celebrators storming onto the street?
With the advent of synthetic data and edge-case emphasis, computer vision systems can be trained to understand these rare exceptional cases, thus improving vehicle quality and overall safety.
Besides, computer vision systems have become better at identifying transparent objects, by a mid-step process of converting the object into its opaque version, before being fed into the system.
Improvements to public safety
The governments of nations from India to France are planning to implement computer vision cameras across streets, to curb crime. Computer vision has shown that it can be used to identify missing individuals, monitor criminal activities in real-time, and locate areas that require additional police attention.
The financial services industry has discovered the potential of Big Data and Machine Learning. Money transfer companies are discovering the importance of competitive intelligence, for increasing churn rate and exploring multiple avenues for payment-based opportunities.
Also, AI helps financial firms mitigate fraudulent transactions and provides more accurate ways of assigning credit scores.
Laws surrounding AI
Another contribution to AI’s innovation is the governments’ understanding of AI’s capabilities and potential threat. The EU announced that it will restrict the development of high-risk AI applications. The type that might hurt employment opportunities and reduce the quality of human life.
Lawmakers are beginning to understand that not all AI development will benefit society, thus allowing only the most important and life-satisfying innovations to see the light of day.
Accuracy in patent visualization
As AI innovations increase, so do the patents filed. Patent visualization platforms hold large data that can allow patent holders to identify potential users, and for tech developers to adhere to patent-related legislation.
Predictive medicine in healthcare
With the recent coronavirus outbreak, AI developers are looking for ways to use machine learning to track the spread of a virus. ML is also being used to identify health-related patterns and symptoms in patients, for implementing predictive medicine and treating avoidable diseases.
Improving workplace performance
Many factors add up to determine results at the workplace. Businesses are now using AI for inventory management, evaluating employee productivity, and identifying flaws in ongoing workflows.
AI in inventory management can be used to warn teams about shortages in resources. With the right algorithms, AI can also replace these resources on time and ensure no outages from its end. Employees can be monitored and areas of improvement, skill, and bandwidth, can be identified to make the best use of their abilities.
Music and entertainment
AI and Big Data are entering subjective fields such as the arts. Musicians can now use AI to understand trends in Billboard charting music, and even mimic traits of popular music to improve music streams and ticket sales.
Mobile gaming has benefited from augmented reality, thus creating virtual scenarios in the real world. In filmmaking, facial expressions can be adjusted and synthetic video effects can be introduced for a more vivid watching experience.
AI is developing at an incredible pace. We are seeing countries across the globe planning AI programs. An AI race between the US and China has begun, with Europe looking to give them a run for their money.
And, our understanding of AI has improved significantly, as shown by the numerous applications we have discovered for them. From deepening our technological knowledge to having an idea of AI could turn rogue, we are becoming mature advocates of AI.
The purpose of AI is to make the lives of humans simpler and add value. Hopefully, in a year from now, we’ll have even more to celebrate as AI strengthens its position as a technological juggernaut.
Artificial Intelligence and innovative services and products are spreading like fire. The companies and individuals who are a fan of technological developments follow the developments. AI provides multiple services and people unaware of new technology even use it extensively.
The modern approach towards the finance industry is the result of multiple technological interventions.
Current Market Size of the Finance Industry:
Presently the expansion phase of the finance sector in India is calling for innovation. The foreign portfolio investors have reached $899.12 million on November 22, 2018. Total Asset Under Management (AUM) in the Mutual Fund industry was on peak, at $340.48 in April 2018 till February 2019. IPOs (Initial Public Offers) raised in the period from April to June 2018 have increased to $1.2 billion.
Investments and Developments provide a new horizon to the upcoming future.
Future of Financial Services:
Leading financial services firms are achieving a higher market share with the AI initiatives they enroll. The finance sector is enthusiastic,about 70% of firms are part of the ML and 60% use NLP. The future of this sector varies in terms of revenues independent of the technology.
Artificial Intelligence brings dependability in the service sector and the finance industry is a prime service provider. The trust built over the last few years is changing the budgeting and strategy for involving AI. It provides an advantage to meet customer expectations and to gain a competitive advantage over others.
The scope of investments by 45% of frontrunner financial services firms are nearing to $5M. Risk takers are likely to win, as they are pro technological changes.
AI adoption increases the ability to solve the operational problems of a repetitive nature, or simple tasks like primary conversations with the basic level of Artificial Intelligence technology. Advanced level of AI brings in understanding power, perception and decision power.
The Association of Mutual Funds in India (AMFI) is targeting nearly five-fold growth in assets under management (AUM) to Rs 95 lakh crore (US$ 1.47 trillion) and three times growth in investor accounts to 130 million by 2025.
Artificial Intelligence helps in credit decisions, risk management, fraud prevention, trading, personalized banking, process automation and enhancement of customer experience. AI is making the dream come true for the people who had weird ideas that machines can do wonders.
Humans are optimistic about AI technology, with expectations that it will bring transactional security, improved digital assistants, a high level of transparency in handling accounts, introducing process automation and foremost significant is the thorough checks of transactions and processes.
Types of Financial Services:
Software and mobile applications are improving accessibility to financial services and Artificial Intelligence is easing the process. Availing services was never so easy. Automation with AI, ML and NLP is a boon for service recipients.
Scope for AI-based automation:
1. Commercial banking Services: These financial services help businesses to raise money from market sources like bonds and equity. The primary activities of commercial banks if powered with AI can bring discipline to internal banking processes. Investment banking and retail banking are already exploring AI.
2. Venture Capital: It is a service that provides outside investors to companies with the potential of high growth. There is a surety of business when these investors bring in money for the business. AI can help in calculating risks and returns for the investors.
3. Angel Investment: An informal investor (angel investor) typically shares the resources and funds their investment capital. There are groups and networks of angel investors. AI can improve networking for connecting the right investment seekers and investors based on preferences.
4. Conglomerates: A financial services company is functioning in multiple sections that provide services such as life insurance, asset management, retail management, and investment banking can draw advantages with AI-based support apps.
5. Financial Market Utilities: Stock exchanges, clearinghouses and interbank networks and such organizations provide specialized services that require precision. AI-powered trading and banking are in high demand.
AI can assist in simplifying the service and improving its quality.
1. Smart Sales: The AI-based Chatbots are better in solving basic queries and responding using FAQs. With no or minimum human intervention, a virtual salesperson can take the customer through the stages of sales right from inquiry until closure.
2. Compliance: An enormousamount of financial data that is generated in banking and other financial services sector creates challenges for the service providers. Ai can identify the malpractices, manipulation and any loopholes found in personalized and classified services.
3. Evaluate Risks: Artificial Intelligence can consider the concerns and treat the user requests accordingly. Each financial transaction, loan or investment is accompanied by various risks that affect the business and thus the help of technology improves decision making.
4. Trading: Financial markets are prone to fluctuations yet many algorithms that try predicting the trends, using the old data. It can independently suggest, buy, sell or hold the stocks and notify us for the transactions or alert based on fed instructions.
5. Predictive Analytics: The spending habits, purchase frequency, other choices, investment portfolio, and transactional data lets AI guide for improving financial decisions and shares investment ideas.
6. Data Enrichment: Transaction data is simplified enough for the customers to understand and take control over their spending habits, budgeting, managing the credit score.
7. Smart Loans: The banks and financial institutes consider the credit score of the customer to approve the loans. Their banking history, income, tax payments, current financial situation, and past loan records are maintained by AI. It can easily bridge the gaps between creditworthy loan seekers and lenders.
8. Personalized Wealth Management: This service is for customers that have either huge bank balances or active investors in both the cases they are the favorite sales targets. The AI-based advisors provide the best advice to the customers based on the customer data available.
AI Performed Banking Activities:
1. Issue checkbooks
2. Credit cards
3. Interacting with customers for balances
4. Loan information and procedures
5. Online transactions
6. Electronic fund transfer
7. Pending documents
8. Send dispatch information
9. Make bill payments
10. Schedule payments
11. Utility bills
12. Repayment of loans
13. Assist in tax planning
14. Aid in foreign exchange
15. Foreign exchange processing and remittance
16. Send info on upcoming investment options in debt and equity
17. Calculate and inform about brokerage for transactions
18. Guidance for wealth management
19. Help buy an insurance policy, send quotes and renewals
20. Book new FDRs and renewal of FDRs
21. Ease to operate the accounts
Innovations that have changed this industry with traditional mindset functions are:
Cleo: An AI-powered data-driven messenger helps manage their finances. It allows the users to link bank accounts and send money to their contacts of FB messenger. You can set a limit for savings and Cleo can keep that spare amount aside. Checks if you should spend money and is it affordable. It can warn users when they do not follow the financial limits and overspend.
ZestFinance: This ML automated platform is an underwriting solution that assesses borrowers with no credit information. AI-powered platform can be implemented by the companies to automate lending and reduce losses occurring due to inaccurate data. Zest Finance can predict the risk and improve the business.
Scienaptic Systems: It provides an underwriting platform that gives banks and credit institutions better transparency about the customers. It successfully holds 10 crores of customers. Scienaptic Systems uses myriad unstructured and structured data, transforms the data and learns from interactions to offer contextual underwriting intelligence. It could save $151 million of loss for a major credit card company.
Eva Money: This AI-based mobile app is available on iOS and Android. It is voice and chat enabled and replies to all your queries relating to personal finances. Link the Eva Money app to your bank accounts and it provides a picture of your current financial holdings. It can even recommend increasing savings, improving credit score and other financial decisions.
Trim: It analyzes your expenses and assists in saving money. It can even cancel the unused facilities or high-cost subscriptions, get you better options on investment and insurance requirements and even negotiate bills for you. VentureBeat reported Trim to save $6.3 million of 50,000 users.
DataRobot: It provides machine learning-based software for data scientists, business analysts, software engineers, and IT professionals. DataRobot helps to build accurate predictive models that can enhance decision making for financial services. It deals with issues like fake credit card transactions, digital wealth management, direct management, and lending.
WinZip: AI-powered finance app delivers automated financial services like investments, savings, and payments. The conversational AI ‘Misa’ is the most powerful financial chatbot, MintZip takes the support of Misa to consider the behavioral sciences and financial sciences for continuous training on financial aspects. It assists users in financial planning.
Kesho: This software provides machine intelligence and data analytics to leaders in the finance industry. Kesho also used cloud computing and NLP, this speeds up the response to the questions from users. Kesho could predict the pound rate drop as mentioned in Forbes article.
AlphaSense: This AI-powered search engine serves the banks, investment firms, and Fortune 500 companies. Natural language processing analyzes keyword search within research, news, and transcripts to discover the trends of financial markets. AlphaSense is providing great value to financial professionals, organizations, companies, traders, and brokers with the latest information on private and public companies. AI analyzes large and complex data and uses algorithms for quantitative trading that can automate trade and make them profitable.
Kavout: It uses machine learning and quantitative analysis to process massive data that is unstructured. Identifies financial market patterns for price and SEC filings in real-time. Higher Kai Score shows outperformance of stock, it is an AI-powered stock ranker. Kavout selected stocks to have a higher annual growth rate.
Kasisto: A conversational AI platform Kai improves customer experiences and reduces the volume of customers approaching call centers. Kasisto is the creator of KAI, a conversational AI platform used to improve customer experiences in the finance industry. Chatbots recommend daily financial decisions based on calculations. Kaisisto can be integrated with mobile apps to provide real-time support to customers.
Shape Security: Top banks in the US use Shape Security restrains frauds of credit application, credential stuffing, cracking gift cards and other frauds by investigating and identifying fake users. The ML models are trained to identify between real customers and bots. Its Blackfish network uses AI-enabled bots to detect logins from different IPs, machines, and phones and alerts the customers and companies for the breach.
Able AI: A virtual financial assistant that integrates with Google Home, Amazon Alexa, SMS, Facebook, web, and mobile to make banking more convenient. Able AI provides services like customer support, personal financial management, and conversational banking. The app also helps in budgeting, tracking expenses and working on savings.
AI-based financial services mobile app development is in full swing. Customers are focusing on the things that make a difference in their lives instead of looking for processes and trying to understand the terminology.
The evolution of financial services with the advancement of AI technology allows us to manage business risks, improves forecasts, assists trading, provides cybersecurity, detects fraud, betters personal banking and brightens user experience.
The technology of today is the future of industries tomorrow. Hammer the iron when it is hot applies to the adoption of advanced technology in every sector and finance is no exception. Financial services are awaiting a brighter future where humans are relieved of the pressure to perform better. Let AI guarantee the uninterrupted services for your valuable customers.
Science fiction has always depicted AI as a kind of machine that can communicate and behave like humans in society. While research on artificial intelligence had already begun by the late 1950s, it wasn’t until the advent of Big Data that AI picked up and became the technological behemoth it is today.
Artificial intelligence has expanded into countless use-cases and industries, enriching the lives of humans globally. AI started as small computer programs that could play checkers and solve word problems. It has since grown into being able to predict stock markets and election results. While we still don’t have talking robots, or at least not too many, we do have easy access to chatbots. And, some of them have developed into the most strange but interesting use-cases for artificial intelligence the world has ever seen.
While chatbots may seem human, it’s important to note that they are just lines of code, a string of 1s and 0s floating through cyberspace. However, what they lack in human behavior, they make up for with superior natural language processing, neural networks, and deep learning. This makes chatbots awesome devices that can simulate humans and keep them hooked. Here are some of the most popular ones:
It’s hard to talk about chatbots without mentioning ALICE: Artificial Linguistic Internet Computer Entity. It is the first chatbot ever popularized on the web. Developed by Dr. Richard Wallace over 25 years ago, ALICE holds up reasonably well to this day. While her answers don’t always hit the mark, it’s easy to forget how old this code is after even a short conversation with ALICE.
Sometimes ALICE gives answers that sounded more futuristic than expected at the time. People will remember ALICE for how it spurred the development of numerous, arguably more advanced chatbots over the next couple of decades. ALICE was also the inspiration for the AI computer in the 2013 Academy Award-winning science-fiction film, Her.
Designed to help Alzheimer’s patients feel less lonely Endurance is one of the most helpful chatbots for older individuals who suffer from short-term memory loss and dementia. This open-source chatbot was created to identify deviations in conventional branches that could mean problems for immediate recollection, which can be quite the challenge for natural language processing systems.
Anyone can contribute to the project’s code-base. Though it isn’t fully fleshed out yet, it could potentially provide researchers and scientists more insight into how memory loss works, and how it can be helped. Since the platform is based on the cloud, doctors and family members can review communication logs instantly. This helps identify potential degradation in short-term memory. Further, it takes the burden off family members to constantly be available as a companion.
Global child advocacy non-profit organization, United Nations Children’s Fund (UNICEF) is now using chatbots to help citizens in developing countries to speak out about their communities’ urgent needs. The U-Report chatbot enables large-scale data gathering via polls. By regularly sending out polls prepared according to a range of urgent social issues, users can respond with what needs to be tackled first. UNICEF is then able to use this feedback to create or amend potential policy recommendations.
In Liberia, this rather basic chatbot was able to create major impact waves by finding out whether teachers were convincing students to have sex in exchange for better grades. 86% of the 13,000 Liberian children involved responded about the issues, which led to a collaborative project between UNICEF and Liberia’s Minister of Education to eradicate the problem.
Woebot is a chatbot with a cartoon mascot that helps reduce depression through active listening. Psychologist Alison Darcy developed it at Stanford University. It also praises the user with funny GIFs, memes, and encouragement. The New York Times, Wired, and Business Insider has featured this chatbot multiple times over the years.
They praised its, “gentle attentiveness and sensitivity to emotions.” The chatbot is available to those who need a confidence boost, guidance, or even just a friend. Woebot is available for download on Google Play Store and Apple’s App Store, and users have applauded its funny and likable personality, despite some scripted lines popping up here and there.
Created by Casper, which specializes in mattresses and pillows, this is the most well-named chatbot on the list. Casper developed Insomnobot 3000 to help insomniacs find someone to talk to as they lie awake in bed. The chatbot’s website reads, “A friendly, easily distracted bot designed to keep you company when you just can’t fall asleep. Extra chatty between 11 pm-5 am.”The interface is also much more intuitive, as users don’t need to visit a particular website or download an app to talk to the chatbot. Instead, they are required to text the number provided on the website, similar to how you would text a friend late at night. The bot does bring up some product suggestions here and there, but it never becomes overbearing. It was also included on Forbes as one of the most amazing examples of online chatbots in practice.
Everyone cannot afford legal counsel, especially when wrongly accused. DoNotPay is a chatbot developed to help users dispute parking tickets, by guiding you through the process without any lawyer interaction.
If user reviews are to be believed, the service has been quite successful at achieving its goal and empowers users to settle legal matters from the palm of their hands. It’s also helping individuals actively learn about their rights as citizens. While this isn’t the most conversational chatbot, it serves its purpose well and is one of the most strange but interesting use-cases of artificial intelligence.
It is a networking Legal and HR chatbot that business enterprises use to solve many issues relating to the recruitment, legal paperwork, etc. smoothly. Robot Vera can improve the workflow and productivity of companies.
Robot Vera assists HR teams by automatically analyzing resume databases and calling candidates that make a good fit. The conversational AI filters out the applications received for a position to merely 10% of the best suitable ones. It then informs candidates about the job description, schedules and conducts telephonic interviews or video chats.
Robot Vera can evaluate the answers of applicants and perform recruitment tasks almost 10 times faster than humans perform. It can hire employees, handle complex office situations, and even fire them if needed.
BabyCentre UK belongs to Johnson & Johnson, a reputed name in childcare. This bot helps answer questions about pregnancy and childbirth. It can calculate the due date of would-be mothers and guide them for preparing for childbirth. It also provides many resources on self-care for moms going through all stages of motherhood.
The bot provides information on how to get pregnant, weekly articles during pregnancy, health care of the mother and the baby, when to call a doctor for your baby, why your baby does not sleep, among many others. It also provides mothers with a forum to share their doubts and experiences.
IBM, a leader in the computer manufacturing space, developed this advanced chatbot. It holds the content of varied industries and is pre-trained for industry-specific use cases, using data relevant to that industry. It can understand historical chat, call logs, search queries and also respond to issues using its knowledge base.
The Watson Assistant can be part of your company’s website, messaging channels, customer service tools adapted, and your mobile apps. The chatbot offers a visual dialog editor making your zero experience of coding your power in developing a new feature.
Replika is a virtual companion chatbot that intends to promote mental wellness. Users can complain or talk about anything, from their failed relationships to their deepest desires. Replika helps people meet their emotional needs. It soothes them when they feel anxious or heartbroken and need an inspiring or comforting chat. Users can select various options for self-motivation, depending on their choice.
As chatbots continue to become an integral part of our society, the trust placed on user privacy and data collection only increases. Today chatbots can send you money, order your pizza, and as shown above, even settle your legal disputes. These can be frightening from the perspective of AI becoming intelligent enough to make humans obsolete. And this is a question developers keep in mind and debate frequently within the AI community.
Whether you’re tracking short-term memory loss or just need a friend, there is a chatbot out there for you. And they continue to learn with every conversation they have.. They’re on Facebook Messenger, Reddit, and even Slack. The last time you opened a chat with a customer service representative, an AI chatbot most likely handled it. If Google Duplex has shown us anything, it’s that AI might become a much more integral part of our lives than previously envisioned.
Humans have become more comfortable with chatbots and AI. Artificial intelligence systems are learning more about how humans communicate, behave and function. Perhaps we can live in a world where humans and machines live in harmony. After all, most of the time, we just need someone to talk to.
Artificial Intelligence a popular technology of computer science is also known as machine intelligence. Machine Learning is a systematic study of algorithms and statistical models.
AI creates intelligent machines that react like humans as it can interpret new data. ML enables computer systems to perform learning-based actions without explicit instructions.
AI global market is predicted to reach $169 billion by 2025. Artificial Intelligence will see increased investments for the implementation of advanced level software. Organizations will strategize technological advancements.
Various platforms and tools for AI and ML empower the developers to design powerful programs.
Tools for AI and ML:
Google ML Kit for Mobile:
Software development kit for Android and IOS phones enables developers to build robust applications with optimized and personalized features. This kit allows developers to ember the machine learning technologies with cloud-based APIs. This kit is integration with Google’s Firebase mobile development platform.
On-device or Cloud APIs
Face, text and landmark recognition
Detect and track object
AutoML Vision Edge
AutoML Vision Edge allows developers to train the image labeling models for over 400 categories it capacities to identify.
Smart Reply API suggests response text based on the whole conversation and facilitates quick reply.
Translation API can convert text up to 59 languages and language identification API forms a string of text to identify and translate.
Object detection and tracking API lets the users build a visual search.
Barcode scanning API works without an internet connection. It can find the information hidden in the encoded data.
Face detection API can identify the faces in images and match the facial expressions.
Image labeling recognizes the objects, people, buildings, etc. in the images and with each matched data; ML shares the score as a label to show the confidence of the system.
Custom models can grow in huge sizes.
Beta Release mode can hurt cloud-based APIs.
Smart reply is useful for general discussions for short answers like “Yes”, “No”, “Maybe” etc.
AutoML Vision Edge tool can function successfully if plenty of image data is available.
This Machine Learning framework is designed for building applications that require pattern recognition, computer vision, machine listening, and signal processing. It combines audio and image processing libraries written in C#. Statistical data processing is possible with Accord. Statistics. It can work efficiently for real-time face detection.
Algorithms for Artificial Neural networks, Numerical linear algebra, Statistics, and numerical optimization
Problem-solving procedures are available for image, audio and signal processing.
Workflow Automation, data ingestion, speech recognition,
Accord.NET libraries are available from the source code and through the executable installer or NuGet package manager.
With 35 hypothesis tests including two-way and one-way ANOVA tests, non-parametric tests useful for reasoning based on observations.
It comprises 38 kernel functions e.g. Probabilistic Newton Method.
It contains 40 non-parametric and parametric statistical distributions for the estimation of cost and workforce.
Real-time face detection
Swap learning algorithms and create or test new algorithms.
Support is available for. Net and its supported languages.
Slows down because of heavy workload.
A flexible architecture allows users to deploy computation on one or multiple desktops, servers, or mobile devices using a single API.
Runs on one or more GPUs and CPUs.
It’s yielding scheme of tools, libraries, and resources allow researchers and developers to build and deploy machine-learning applications effortlessly.
High-level APIs accedes to build and train for ML models efficiently.
Runs existing models using TensorFlow.js, which acts as a model converter.
Train and deploy the model on the cloud.
Has a full-cycle deep learning system and helps in the neural network.
You can use it in two ways, i.e. by script tags or by installing through NPM.
It can even help for human pose estimation.
It includes the variety of pre-built models and model subblocks can be used together with simple python scripts.
It is easy to structure and train your model depending on data and the models with you are training the system.
Training other models for similar activities is simpler once you have trained a model.
The learning curve can be quite steep.
It is often doubtful if your variables need to be tensors or can be just plain python types.
It restricts you from altering algorithms.
It cannot perform all computations on GPU intensive computations.
The API is not that easy to use if you lack knowledge.
This self-learning knowledge-based AI platform accumulates organizational data from people, business processes and legacy systems. It is designed to engage in complicated business tasks to forecast revenues and suggest profitable products the company can introduce.
Business Knowledge Processing
Robotic Process Automation
Organizational Transformation is possible with enhanced technologies to automate and increase operational efficiency.
It enables organizations to continually use previously gained knowledge as they grow and even modify their systems.
Faster data processing adds to the flexibility of data visualization, analytics, and intelligent decision-making.
Reduces human efforts involved in solving high-value customer problems.
It helps in discovering new business opportunities.
It is difficult to understand how it works.
Extra efforts needed to make optimum use of this software.
It has lesser features of Natural Language Processing.
Mainly it aims towards implementing and executing algorithms of statistics and mathematics. It’s mainly based on Scala and supports Python. It is an open-source project of Apache. Apache Mahout is a mathematically expressive Scala DSL (Domain Specific Language).
It is a distributed linear algebra framework and includes matrix and vector libraries.
Common maths operations are executed using Java libraries
Build scalable algorithms with an extensible framework.
Implementing machine-learning techniques using this tool includes algorithms for regression, clustering, classification, and recommendation.
Run it on top of Apache Hadoop with the help of the MapReduce paradigm.
It is a simple and extensible programming environment and framework to build scalable algorithms.
Best suited for large datasets processing.
It eases the implementation of machine learning techniques.
Run-on the top of Apache Hadoop using the MapReduce paradigm.
It supports multiple backend systems.
It includes matrix and vector libraries.
Deploy large-scale learning algorithms using shortcodes.
Provide fault tolerance if programming fails.
Needs better documentation to benefit users.
Several algorithms are missing this limits the developers.
No enterprise support makes it less attractive for users.
At times it shows sporadic performance.
It provides various algorithms and data structures for unified machine learning methods. Shogun is a tool written in C++, for large-scale learning, machine learning libraries are useful in education and research.
Huge capacity to process samples is the main feature for programs with heavy processing of data.
It provides support to vector machines for regression, dimensionality reduction, clustering, and classification.
It helps in implementing Hidden Markov models.
Provides Linear Discriminant Analysis.
Supports programming languages such as Python, Java, R, Ruby, Octave, Scala, and Lua.
It processes enormous data-sets extremely efficiently.
Link to other tools for AI and ML and several libraries like LibSVM, LibLinear, etc.
It provides interfaces for Python, Lua, Octave, Java, C#, C++, Ruby, MatLab, and R.
Cost-effective implementation of all standard ML algorithms.
Easily combine data presentations, algorithm classes, and general-purpose tools.
Some may find its API difficult to use.
It is an open-source tool for data mining and data analysis, developed in Python programming language. Scikit-Learn’s important features include clustering, classification, regression, dimensionality reduction, model selection, and pre-processing.
Consistent and easy to use API is also easily accessible.
Switching models of different contexts are easy if you learn the primary use and syntax of Scikit-Learn for one kind of model.
It helps in data mining and data analysis.
It provides models and algorithms for support vector machines, random forests, gradient boosting, and k-means.
It is built on NumPy, SciPy, and matplotlib.
BSD license lets you use commercially.
Easily documentation is available.
Call objects to change the parameters for any specific algorithm and no need to build the ML algorithms from scratch.
Good speed while performing different benchmarks on model datasets.
It easily integrates with other deep learning frameworks.
Documentation for some functions is slightly limited hence challenging for beginners.
Not every implemented algorithm is present.
It needs high computation power.
Recent algorithms such as XGBoost, Catboost, and LightGBM are missing.
Scikit learns models take a long time to train, and they require data in specific formats to process accurately.
Customization for the machine learning models is complicated.
Twitter, Facebook, Amazon, Google, Microsoft, and many other medium and large enterprises continuously use improved development tactics. They extensively use tools for AI and ML technology in their applications.
Various tools for AI and ML can ease software development and make the solutions effective to meet customer requirements. Make user-friendly mobile applications or other software that are potentially unique. Using Artificial Intelligence and Machine Learning create intelligent solutions for improved human life. New algorithm creation, using computer vision and other technology and AI training requires skills and development of innovative solutions that need powerful tools.
Computer vision refers to the field of training computers to visualize data as humans do. This technology has the potential to reach a stage wherein computers can understand images and videos better than humans. Also, the use cases are practically limitless, despite the technology still existing in its nascent stage of exploration.
Computer vision as a concept has been around since the 1950s. In its infancy, computers were trained to distinguish between shapes such as squares and triangles. Later on, training shifted towards distinguishing between typed and handwritten text.
Reasons for popularity
The main reason for computer vision’s popularity is its potential to revolutionize many every-day aspects of our lives. Computer vision drives autonomous vehicles and allows them to distinguish between traffic signal lights, medians, pedestrians, etc. It can also be used in healthcare, for detecting tumors in advance and identifying skin issues.
There is a huge opportunity for employing computer vision in agriculture as well. It can be used to monitor the quality of crops, locate weeds and pests, based on which farmers can take action.
How about facial recognition? Yes, computer vision is already being used in new-generation smartphones to detect the user’s face. Even QR code scanning is an example of the adoption of computer vision. This technology can also be used in supermarkets to identify which users are making which purchases.
Amazon is testing a convenience store called Amazon Go, which doesn’t have a billing counter. Instead, the store uses computer vision to identify customers and the items they add to their cart. A bill is sent to them online through the Amazon Go App once they leave the store with these items.
Advantages of computer vision
While computer vision has a lot more to achieve, it has already achieved ground-breaking innovations. That makes sense because this technology brings a lot of advantages to daily and professional life.
The human eye grows tired of scanning its environment. Factors such as fatigue and health come into the picture. With computer vision, this is eliminated because cameras and computers never get tired. Since the human factor is removed, it is easier to rely on the result.
Numerous use cases
From healthcare and agriculture to banking and automobiles, if explored smartly, computer vision can be employed in almost every aspect of our lives. These machines learn by viewing thousands of labeled images, thus understanding the traits of what’s being visualized. The same primary computer vision technology that evaluates the quality of packages in a factory can also be used to identify trends in the stock market.
Computer vision can be used to increase productivity in operations and eliminate faulty products from hitting the shelves. This technology will also allow companies to manage their teams efficiently by identifying staff that could be used for other activities that require attention. For example, in Amazon fulfillment centers, productivity among workers is measured to improve efficiency and resource allocation.
Challenges faced by Computer Vision
Every emerging technology starts with a few significant drawbacks. From this technology’s development to its impact on society, there is a lot to look forward to, but a lot to be concerned about as well.
The challenge of making systems human-like
As much as computer vision is making huge leaps in its progress, it is difficult to simulate something as complex as the human visual system. The human brain-eye coordination is a marvel to behold, and its ability to understand its environment and make decisions is unparalleled by computer vision systems, at least at the moment.
Tasks such as object detection are complicated since objects of interest in images and videos may appear in a variety of sizes and aspect ratios. Also, a computer vision system will have to distinguish one object from multiple others within its view. This is a skill that computers are taking time to get better at.
Computer vision also hasn’t reached the stage wherein it can identify the difference between handwritten and typed text. This is due to the variety of handwriting styles, curves, and shapes employed while writing.
This is arguably the biggest social threat that computer vision poses. The qualities that make computer vision effective are also the concerns of humans that value their privacy. With computers learning from thousands and thousands of images and videos, computers are getting better at recognizing individuals by their facial features, and everyone’s information is stored on a cloud.
Computer vision can track people’s whereabouts and monitor their habits. With such information, governments and businesses could be lured into penalizing and rewarding workers based on their actions. China, a nation with strong AI capabilities, is already looking to use computer vision to monitor its citizens and provide information that funds its controversial social credit system. On the other hand, San Fransisco has banned the use of facial recognition technology by the police and other related agencies.
It is psychologically unhealthy for humans to know that they are constantly being observed and monitored during every aspect of their lives. It would be interesting to see how governments intend to tackle this issue.
Computer vision’s progress can make people truly feel like they’re living through a sci-fi film. The future of this technology is filled with a range of use cases to be catered to. Numerous businesses within this realm are collecting millions of images and videos that can be used to train their computer vision systems. Also, existing businesses are exploring ways to employ computer vision into their operations.
Computer vision has its present challenges, but the humans working on this technology are steadily improving it. Every emerging technology brings its fair share of advantages and disadvantages. While it is important to celebrate its progress, it is equally important to gauge its potential negative effect on society. This is the only way to ensure that computer vision makes our lives more comfortable and less constrained.
In the previous couple of years, computerized reasoning has progressed so rapidly that it presently appears to be not a month passes by without a newsworthy Artificial Intelligence (AI) achievement. In territories as wide-running as discourse interpretation, medicinal analysis, and interactivity, we have seen PCs beat people in frightening manners.
This has started an exchange about how AI will affect work. Some dread that as Artificial intelligence improves, it will replace laborers, making a consistently developing pool of unemployable people who can’t contend monetarily with machines. This worry, while reasonable, is unwarranted. Truth be told, AI will be the best employment motor the world has ever observed.
2020 will be a significant year in AI-related work elements, as indicated by Gartner, as AI will turn into a positive employment helper. The number of occupations influenced by Artificial Intelligence will shift by industry; through 2019, social insurance, the open division, and instruction will see constantly developing employment requests while assembling will be hit the hardest. Beginning in 2020, AI-related occupation creation will a cross into positive area, arriving at 2,000,000 net-new openings in 2025, Gartner said in a discharge.
Numerous huge advancements in the past have been related to change the time of impermanent occupation misfortune, trailed by recuperation, at that point business change and AI will probably pursue this course.
JOBS CREATED BY AI AND MACHINE LEARNING
A similar idea applies to AI. It is an instrument that individuals need to figure out how to utilize and how to apply to what’s going on with as of now. New openings are now being made that are centered around applying AI to security, improving basic AI methods, and on keeping up these new apparatuses.
Plenty of new openings will develop for those with mastery in applying center Artificial Intelligence innovation to new fields and applications. Specialists will be expected to decide the best sort of AI (for example master frameworks or AI), to use for a specific application, create and train the models, and keep up and re-train the frameworks as required. In fields, for example, security, where sellers have enabled security programming with AI, it’s up to clients – the security investigators – to comprehend the new capacities and put them to be the most ideal use.
Training is another field where AI and machine learning is making new openings. As of now, over the US, the main two situations in the rundown of scholastic openings are for Security and Machine Learning specialists. Colleges need more individuals and can’t discover educators to show these fundamentally significant subjects.
FUTURE JOBS PROSPECTS BECAUSE OF AI AND MACHINE LEARNING
In a few businesses, AI will reshape the sorts of employments that are accessible. What’s more, much of the time, these new openings will be more captivating than the monotonous errands of the past. In assembling, laborers who had recently been attached to the generation line, looking for blemished items throughout the day, can be redeployed in increasingly profitable interests — like improving procedures by following up on bits of knowledge gathered from AI-based sensor and vision stages.
These are increasingly specific errands and retraining or uptraining might be important for laborers to successfully satisfy these new jobs — something the two organizations and people should address sooner than later.
Man-made intelligence-based arrangements in any industry produce monstrous measures of information, frequently from heterogeneous sources. Successfully saddling the intensity of this information requires human abilities. Profound learning researchers have come to comprehend that setting is basic for preparing powerful AI models — and people are important to clarify this information to give set in uncertain circumstances and help spread all this present reality varieties an AI framework will experience.
Keeping that in mind, Appen utilizes more than 40,000 remote contractual workers a month to perform information explanation for our customers, drawing from a pool of more than 1 million talented annotators around the world.
These occupations wouldn’t exist without the profound learning innovation that makes AI conceivable. As researchers and designers make immense advances in innovation, organizations and laborers may need to adopt new mechanical aptitudes to remain aggressive.
Simulated intelligence is helping drive work creation in cybersecurity
As the worldwide economy is progressively digitized and mechanized, effectively unavoidable criminal ventures – programmers, malware, and different dangers – will develop exponentially, requiring engineers, analyzers, and security specialists to alleviate dangers to crucial open framework and meet expanding singular personality concerns.
In the previous couple of years there has been an enormous increment in cybersecurity work postings, a large number of which stay unfilled. With this deficiency of cybersecurity experts, most security groups have less time to proactively protect against progressively complex dangers. This interest has made a significant specialty for laborers to fill.
The stream down impact of industry-wide digitalization
In a roundabout way, the efficiencies and openings that profound learning and computerization empower for organizations can make a great many employments. While mechanized conveyance strategies, for example, self-driving conveyance trucks will take a great many drivers off the street, an ongoing Strategy + Business article proposes that, “In reality as we know it where organizations are progressively made a decision on the nature of the client experience they give, you will require representatives who can consolidate the aptitudes of a client care specialist, advertiser, and sales rep to sit in those trucks and connect with clients as they make conveyances.”
Additionally, the higher profitability and positive development empowered by AI will positively affect employing as organizations will just need to procure more laborers to take on existing assignments that require human abilities. Consider client support, publicists, program administrators, and different jobs that require abilities, for example, compassion, moral judgment, and inventiveness.
Growing new aptitudes to endure and flourish
It’s anything but difficult to perceive any reason why laborers and administrators the same may be hesitant to execute AI-controlled mechanization. Be that as it may, as their rivals receive this innovation and start to outpace them in deals, creation, and development, it will expect them to adjust. The two organizations and laborers should put resources into developing new innovative aptitudes to enable them to remain significant in this information-driven scene. If they can do this, the open doors for business and expert development are perpetual.
DEVELOPMENT IN THE FIELD OF AI and ML
Man-made reasoning is a method for making a PC, a PC controlled robot, or a product think keenly, in the comparative way the insightful people think. Man-made brainpower is a science and innovation dependent on orders, for example, Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A significant push of Artificial Intelligence (AI) is in the advancement of PC capacities related to human knowledge, for example, thinking, learning, and critical thinking.
AI is a man-made consciousness-based method for creating PC frameworks that learn and advance dependent on experience. Some basic AI applications incorporate working self-driving autos, overseeing speculation reserves, performing legitimate disclosure, making therapeutic analyses, and assessing inventive work. A few machines are in any event, being educated to mess around.
Man-made intelligence and MACHINE LEARNING isn’t the eventual fate of innovation — it’s nowhere. Simply see how voice aides like Google’s Home and Amazon’s Alexa have turned out to be increasingly more unmistakable in our lives. This will just proceed as they adapt more aptitudes and organizations work out their associated gadget biological systems. The accompanying can be viewed as a portion of the significant advancements in the field of AI.
Artificial intelligence in Banking and Payments
This report features which applications in banking and installments are most developed for AI. It offers models where monetary organizations (FIs) and installments firms are as of now utilizing the innovation, talks about how they should approach actualizing it, and gives depictions of merchants of various AI-based arrangements that they might need to think about utilizing.
Computer-based intelligence in E-Commerce
This report diagrams the various uses of AI in retail and gives contextual analyses of how retailers are increasing a focused edge utilizing this innovation. Applications incorporate customizing on the web interfaces, fitting item suggestions, expanding the hunt significance, and giving better client support.
Computer-based intelligence in Supply Chain and Logistics
This report subtleties the variables driving AI appropriation in-store network and coordinations, and looks at how this innovation can decrease expenses and sending times for activities. It likewise clarifies the numerous difficulties organizations face actualizing these sorts of arrangements in their store network and coordinations tasks to receive the rewards of this transformational innovation.
Artificial intelligence in Marketing
This report talks about the top use cases for AI in advertising and looks at those with the best potential in the following couple of years. It stalls how promoting will develop as AI robotizes medicinal undertakings, and investigates how client experience is winding up increasingly customized, pertinent, and auspicious with AI.
To close, AI introduces a colossal open door for venturesome individuals. Representatives have the chance to jump into another field and conceptual their business to another, more significant level of investigation and vital worth. Businesses need to help these moves and for the most part remain open to representatives rethinking themselves as they hold onto innovations, for example, AI.
Artificial Intelligence is here to change the way humans interact with their world, and it’s poised to make life easier. Today, numerous applications of Artificial Intelligence for business solutions exist. From voice assistants playing music at our behest to phones unlocking themselves by viewing our faces, AI has shown us that the future is here.
AI is also here to make life simpler for employees and businesses. A lot of business processes are waiting to be automated, and data analytics is offering more insights than ever for decision making and identifying opportunities. AI can manage a company’s workflow and predict trends.
There are a variety of applications for AI in business. Let’s do a rundown of the eight most popular ones:
Serve your customers better
Every business needs to keep its customers happy and satisfied. They also need to know how to empathize and deal with unhappy ones. A strong customer base is integral to a business’s success, and AI is making it easier to achieve this.
Businesses can use conversational AI to provide a personalized platform for customer interaction. Customers love immediate responses, and research exists to back this up. Econsultancy reports that 79% of customers prefer to chat with a customer support rep to solve issues and queries.
Businesses can employ chatbots to make sure customers always have someone to go to instantly if and when there’s a problem. Chatbots can handle simple queries and lead customers to a human support representative if the issue is complex.
Predict online behavior
Understanding online customer behavior is essential to e-commerce. Factors such as product clicks, bounce rate, purchases, etc. determine the success or failure of products sold by online businesses.
Data analytics allows online businesses to study the data that they’ve captured. It’s a great way to understand which products are helping the business and also the ones that need to be discontinued. New products can also be launched if certain product categories are proving to be popular.
Machine Learning algorithms can track user behavior on websites. With the information collected, businesses can personalize a customer’s experience. Customers could be shown products that they are likely to buy.
Manufacturing businesses can make use of computer vision to monitor factory operations. Such technology can measure employee productivity and the efficiency of processes. Industrial robots can replace repetitive tasks or tasks that eliminate possible human error.
Improve physical checkouts
With the help of computer vision, retail stores can save customers a lot of time while checking out. Computer vision cameras across store premises can identify customers and the items they pic. Once customers are done picking what they require, the retailer can send an invoice online, thus avoiding any reasons to wait in a long queue.
Strengthen your cybersecurity efforts
Every business has data that needs to be protected. They generally store this data on common/public infrastructure, which makes the data more prone to cybersecurity attacks.
Businesses can employ AI/ML to strengthen their cybersecurity efforts. They can use ML to detect malicious activities in data storage systems and improve human analysis, from detecting attacks of a malicious nature to endpoint protection. Also, businesses can automate mundane tasks, thus allowing less room for human error due to fatigue, and more accurate results.
Market yourself with data
With the help of AI and ML, advertising campaigns can be planned with less subjectivity and more data-backed decision making. AI models that can analyze the most successful advertisement campaigns of the past are available in the market (IBM Watson, for example). These models can study advertisement parameters such as audiences, click rate, transaction rate, overall spend, etc.
AI can also identify and segment audiences that are most likely to respond to a certain ad positively. By understanding their audiences, ads creatives, while subjective in nature, can be provided with an objective touch, to increase conversions.
Today, most brands use AI to prepare their ad campaigns. Using data, ads of the future can learn from the past to hack the future in their favor.
Detect fraud and anomalies
The banking industry is a sensitive one since issues in this field affect customers more than any other industry. Now that we’ve got Big Data, banks and financial firms can now access data on customer spending habits. So, if bank officials observe any anomalies in any transaction from a customer’s bank account, they can alert customers.
AI-inspired fraud detection applications review a customer’s social media, employment statistics, high school & college education, etc. to determine whether their expenditures and financial activities are in sync. Businesses can continuously update such applications as customer data change, thus more accurately determining what accounts for financial fraud.
To execute any strategy successfully, the resources that aid the execution process need to be abundant. Outages can slow down industry processes and hamstring operations.
AI can monitor teams and their inventory to determine whether a plan will be executed on time or not. Teams can be alerted if new additions need to be made to their inventory and if any resources aren’t being used effectively.
For example, in a factory setup, monitoring storage locations allows businesses to identify missing items and raw materials that need to be replaced or replenished. These raw materials are crucial to the final product’s creation, and AI can ensure that any possible hurdles are taken care of.
Despite AI being in its nascent stage, it has already proven to be a technological juggernaut. In business, AI can improve manufacturing processes, reduce financial fraud, and improve marketing campaigns, among many other applications as discussed above.
With extraordinary leaps made in machine learning and computer vision, it will be interesting and exciting to see AI developers discover new applications. We will definitely update this piece once further applications of Artificial Intelligence for business present themselves.
Under 70 years from the day when the very term Artificial Intelligence appeared, it’s turned into a necessary piece of the most requesting and quick-paced enterprises. Groundbreaking official directors and entrepreneurs effectively investigate new AI use in money and different regions to get an aggressive edge available. As a general rule, we don’t understand the amount of Machine Learning and AI is associated with our everyday life.
Software engineering, computerized reasoning (AI), once in a while called machine knowledge. Conversationally, the expression “man-made consciousness” is regularly used to depict machines that emulate “subjective” capacities that people partner with the human personality.
These procedures incorporate learning
(the obtaining of data and principles for utilizing the data), thinking
(utilizing standards to arrive at surmised or positive resolutions) and
Machine learning is the coherent examination of counts and verifiable models that PC systems use to play out a specific task without using unequivocal rules, contingent upon models and induction. It is seen as a subset of man-made thinking. Man-made intelligence estimations manufacture a numerical model reliant on test information, known as “getting ready information”, in order to choose figures or decisions without being explicitly adjusted to playing out the task.
Money related hazard is a term that can
apply to organizations, government elements, the monetary market overall, and
the person. This hazard is the risk or probability that investors, speculators,
or other monetary partners will lose cash.
There are a few explicit hazard factors
that can be sorted as a money related hazard. Any hazard is a risk that
produces harming or undesirable outcomes. Some increasingly normal and
particular money related dangers incorporate credit hazard, liquidity hazard,
and operational hazard.
Financial Risks, Machine Learning, and AI
There are numerous approaches to sort an organization’s monetary dangers. One methodology for this is given by isolating budgetary hazards into four general classes: advertise chance, credit chance, liquidity hazard, and operational hazard.
AI and computerized reasoning are set to
change the financial business, utilizing tremendous measures of information to
assemble models that improve basic leadership, tailor administrations, and
improve hazard the board.
Market hazard includes the danger of changing conditions in the particular commercial center where an organization goes after business. One case of market hazard is the expanding inclination of shoppers to shop on the web. This part of the market hazard has exhibited noteworthy difficulties in conventional retail organizations.
of AI to Market Risk
Exchanging budgetary markets naturally includes the hazard that the model being utilized for exchanging is false, fragmented, or is never again legitimate. This region is commonly known as model hazard the executives. AI is especially fit to pressure testing business sector models to decide coincidental or rising danger in exchanging conduct. An assortment of current use instances of AI for model approval.
It is likewise noticed how AI can be
utilized to screen exchanging inside the firm to check that unsatisfactory
resources are not being utilized in exchanging models. An intriguing current
utilization of model hazard the board is the firm yields. which gives ongoing
model checking, model testing for deviations, and model approval, all
determined by AI and AI systems.
One future bearing is to move more towards support realizing, where market exchanging calculations are inserted with a capacity to gain from market responses to exchanges and in this way adjust future exchanging to assess how their exchanging will affect market costs.
Credit hazard is the hazard
organizations bring about by stretching out credit to clients. It can likewise
allude to the organization’s own acknowledge hazard for providers. A business
goes out on a limb when it gives financing of buys to its clients, because of
the likelihood that a client may default on installment.
of AI to Credit Risk
There is currently an expanded enthusiasm by establishments in utilizing AI and AI procedures to improve credit hazard the board rehearses, somewhat because of proof of inadequacy in conventional systems. The proof is that credit hazard the executives’ capacities can be essentially improved through utilizing Machine Learning and AI procedures because of its capacity of semantic comprehension of unstructured information.
The utilization of AI and AI systems to demonstrate credit hazard is certainly not another wonder however it is a growing one. In 1994, Altman and partners played out a first similar investigation between conventional measurable techniques for trouble and chapter 11 forecast and an option neural system calculation and presumed that a consolidated methodology of the two improved precision altogether
It is especially the expanded unpredictability of evaluating credit chance that has opened the entryway to AI. This is apparent in the developing credit default swap (CDS) showcase where there are many questionable components including deciding both the probability of an occasion of default (credit occasion) and assessing the expense of default on the off chance that default happens.
Liquidity hazard incorporates resource liquidity and operational subsidizing liquidity chance. Resource liquidity alludes to the relative straightforwardness with which an organization can change over its benefits into money ought to there be an unexpected, generous requirement for extra income. Operational subsidizing liquidity is a reference to everyday income.
to liquidity chance
Consistency with hazard the executives’ guidelines is an indispensable capacity for money related firms, particularly post the budgetary emergency. While hazard the board experts regularly try to draw a line between what they do and the frequently bureaucratic need of administrative consistence, the two are inseparably connected as the two of them identify with the general firm frameworks for overseeing hazard. To that degree, consistency is maybe best connected to big business chance administration, in spite of the fact that it contacts explicitly on every one of the hazard elements of credit, market, and operational hazard.
Different favorable circumstances noted are the capacity to free up administrative capital because of the better checking, just as computerization diminishing a portion of the evaluated $70 billion that major money related organizations go through on consistency every year.
Operational dangers allude to the different dangers that can emerge from an organization’s normal business exercises. The operational hazard class incorporates claims, misrepresentation chance, workforce issues, and plan of action chance, which is the hazard that an organization’s models of promoting and development plans may demonstrate to be off base or insufficient.
to Operational Risk
Simulated intelligence can help establishments at different stages in the hazard the boarding procedure going from distinguishing hazard introduction, estimating, evaluating, and surveying its belongings. It can likewise help in deciding on a fitting danger relief system and discovering instruments that can encourage moving or exchanging hazards.
Along these lines, utilization of Machine Learning and AI methods for operational hazard the board, which began with attempting to avoid outside misfortunes, for example, charge card cheats, is currently extending to new regions including the examination of broad archive accumulations and the presentation of tedious procedures, just as the discovery of illegal tax avoidance that requires investigation of huge datasets.
We along these lines finish up on a positive note, about how AI and ML are changing the manner in which we do chance administration. The issue for the set up hazard the board capacities in associations to now consider is on the off chance that they wish to profit of these changes, or if rather it will tumble to present and new FinTech firms to hold onto this space.
Artificial Intelligence replicates human intelligence in the automated processes that machines perform. Machines require human intelligence to execute actions. These computer processes are data learning-based and can respond, recommend, decide and autocorrect on the basis of interactions.
Financial Analysis is a process of evaluating business and project suitability, the company’s stability, profitability, and performance. It involves professional expertise. It needs a lot of financial data from the company to analyze and predict.
Finance organizations include entities such as retail and commercial banks, accountancy firms, investment firms, loan associations, credit unions, credit-card companies, insurance companies, and mortgage companies.
Artificial intelligence can teach machines to perform these calculations and analysis just as humans do. We can train machines, the frequency of financial analysis can be set, and accessibly to reports has no time restrictions.
How AI is implemented in Financial
Artificial intelligence adopted by Financial Services is changing the customer expectation and directly influences the productivity of this sector.
Implementation of Artificial intelligence in the Finance Sector:
To streamline processes
Big data processing
Matching data from records
Calculations and reports
Interpretations and expectations
Provide personalized information
Challenges these financial
institutions face in implementing AI is the number of trained data scientists, data
privacy, availability, and usability of data.
Quality data helps in planning and budgeting of automation, standardizing processes, establishing correlation. Natural language processing –NLP used in AI is quite a communicator still with over 100 languages spoken in India and 6500 languages across the globe, the development of interactive sets is challenging.
Add Virtual assistants/ Chatbots to the website, online portals, mobile applications and your page on the social media platform. Chatbots can indulge in basic level conversations, reply FAQs, and even connect the customer to a live agent. Machine Learning technology lowers costs of customer service, operations, and compliance costs of financial service providers. AI provides input to the financial analysts for in-depth analysis.
Advantages of Artificial Intelligence in Financial Analysis:
Mining Big Data: AI uses Big data to improve operational activities, investigation, research, and decision-making. It can search for people interested in financial services and other latest finance products launched in the market.
Risk Assessment: AI can assess investment risks, low-profit risks, and risks of low returns. It can study and predict the volatility of prices, trading patterns, and relative costs of services in the market.
Improved Customer Service: Catering customers with their preset preferences is possible with virtual assistants. Artificial Intelligence understands requests raised by customers and is able to serve them better.
Creditworthiness & Lending: AI helps to process the loan applications, highlights risks associated, crosscheck the authenticity of the applicant’s information, their outstanding debts, etc.
Fraud Prevention: Systems using Artificial Intelligence systems can monitor, detect, trace, and interrupt the identified irregularities. It can identify any transaction involving funds, account access, and usage all that indicate fraud. This is possible with the data processing it does on the historic data, access from new IPs, repetitive errors or doubtful activities and activations.
Cost Reduction: AI can reduce costs of financial services and reduce human efforts, lessens the requirement of resources, and adds to accuracy in mundane tasks. Sales conversion is faster due to the high response rate and saves new customer acquisition costs. Maximizing resources can save time and improve customer service, sales, and performance.
Compliances: Financial data is personal hence, data security, and privacy-related compliances based on norms, rules, and regulations of that region being met. While companies use and publish data, General Data Protection Regulation (GDPR) laws protect individuals and abide by companies to seek permission before they store user data.
Customer Engagement: Recommendations and personalized financial services by AI can meet unique demands and optimize offerings. It can suggest the investment plans considering existing savings, investment choices, habits, and other behavioral patterns, returns expected in percentage as well as in long term or short term, future goals.
Creating Finance Products: AI can help finance industry to create intelligent products from learning’s from the financial datasets. Approaching existing clients for new products or acquiring new is faster with AI technology.
Filtering information: AI helps faster search from a wide range of sources. Search finance services, products, credit-scores of individuals, ratings of companies and anything you need to improve service.
Automation: Accuracy is crucial in the finance sector and while providing financial services. Human decisions are prone to influence of situations, emotions, and personal preferences but AI can follow the process without falling into any loopholes. It can understand faster and convey incisively. Automation of processes can improve with face recognition, image recognition, document scanning, and authentication of digital documents, confirmation of KYC documents, and other background checks; necessary for selective finance services.
Assistance: Text, image and speech assistance helps customers to ask questions, get information, and download or upload documents, connect with company representatives, carry out financial transactions and set notifications.
Actionable items: Based on the financial analysis the insights generated to provide a competitive advantage to the company. A large customer base and its complex data are simplified by AI and send information to the concerned department for scheduling actions. These insights are gathered from all modes of online presence i.e. Website, social media, etc.
Enhanced Performance: Business acceleration, increase in productivity and performance is a result of addition to the AI knowledge base. The overall use of AI technology is adding to opportunities in the finance sector.
Companies utilizing Artificial Intelligence in Financial Analysis:
Niki.ai: This company has worked on various chatbot projects e.g. HDFC bank FB chat provides banking services and attracts additional sales. It created a smartphone application for Federal Bank. Niki the chatbot can guide the customers looking for financial services, e-commerce and retail business with its recommendations. It can assist in end-to-end online transactions for online hotel and cab, flight or ticket booking.
Rubique: It is a lender and applicant matchmaking platform. The credit requirements of applicants are studied before recommendation from this AI-based platform. It has features like e-KYC, bank statement analysis, credit bureau check, generating credit memo & MCA integration. It can track applications in real-time and help to speed up the process.
Fluid AI: It is committed to solving unique and big problems of finance, marketing, government and some other sectors using the power of artificial. It provides a highly accurate facial recognition service that enhances security.
LendingKart: This platform serves by tackling the process of loans to small businesses and has reached over 1300 cities. LendingKart developed technology tools based on big data analysis to evaluate borrower’s creditworthiness irrespective of flaws in the cash flow or past records of the vendor.
ZestFinance: It provides AI-powered underwriting solutions to help companies and financial institutions, find information of borrowers whose credit information is less and difficult to find.
DataRobot: It has a machine learning software designed for data scientists, business analysts, software engineers, and other IT professionals. DataRobot helps financial institutions to build accurate predictive models to address decision-making issues for lending, direct marketing, and fraudulent credit card transactions.
Abe AI: This virtual financial assistant integrates with Amazon Alexa, Google Home, Facebook, SMS, web, and mobile to provide customers convenience in banking. Abe released a smart financial chatbot that helps users with budgeting, defining savings goals and tracking expenses.
Kensho: The company provides data analytics services to major financial institutions such as Bank of America, J.P. Morgan, Morgan Stanley, and S&P Global. It combines the power of cloud computing, and NLP to respond to the complex financial questions.
Trim: It assists customers in rising saving by analyzing their spending habits. It can highlight and cancel money-wasting subscriptions, find better options for insurance and other utilities, the best part is it can negotiate bills.
Darktrace: It creates cybersecurity solutions for various industries by analyzing network data. The probability-based calculations can detect suspicious activities in real-time, this can prevent damage and losses of financial firms. It can protect companies and customers from cyber-attacks.
The future of Artificial Intelligence in Financial Analysis is dependent on continuous learning of patterns, data interpretation, and providing unique services. Financial Analysis and Artificial Intelligence have introduced new management styles, methods of approaching and connecting with customers for financial services. The considerations of choices increase the comfort level of customers and sales. Organizations become data-driven and it helps them to launch, improve, and transform applications.
The insights, accuracy,
efficiency, predictions, and stability have created a positive impact on the
Technology enhancements in computer-human interaction have allowed us to seamlessly interact with computers. Conversational AI has pampered us with privileges such as instant responses, 24/7 access, and a user-friendly medium for conversation. From setting up medical appointments to online check-ins for flights, AI chatbots have gained prominence.
If you’re unaware, a chatbot is a software that can simulate a conversation with a real-life user. It conducts conversation either by chatting or speaking.
The major challenges faced while developing a chatbot include the following:
Developing it to perceive text/voice messages.
Training it to understand how to respond to such messages
Maintaining conversational etiquette
The solution to the above challenges lies in high-quality training data. Training data is the lifeblood of AI/ML models, and its importance is no lesser for conversational AI. Chatbot datasets usually comprise a large volume of query-response pairs (in audio or text) that the chatbot can use for developing its interaction skills.
Here’s why there’s a need for high-quality chatbot training data:
Understanding human language
Human interaction is complicated, and that has a lot to do with how rich and diverse human languages are. This means chatbots need to understand the nitty-gritty of grammar and conversational flow. Conversational datasets allow chatbots to learn from a large number of examples, from which they can learn sentence construction. Such datasets also allow chatbots to learn cases of grammar rule exemptions (as is commonly found in the English language).
As native speakers of a language, we understand which words signify which tones. We understand which statements represent happiness or sadness and pleasure or anger. While these things are simple to us, they need to be ingrained into a chatbot. We can’t have a chatbot responding to an “I’ve been having a bad day.” with an “I’m so happy for you!”
Understanding tone matters a lot while we communicate, and it ought to matter for intelligent beings trying to interact with us.
Clean conversational data
If the training datasets aren’t clean or free of issues, do not expect your AI/ML model to function as intended. With conversational AI, the clarity and cleanness of its training data determines its ability to interact fluently with people.
Common issues with chatbot training data include:
Inaccurate word choices
Unclean conversational datasets usually suffer from grammar issues. Fixing those issues goes a long way in ensuring clean chatbot responses.
Relevant conversational data
Every chatbot is tackling a particular use case. Companies use chatbots for customer service (by food delivery, e-commerce, and banking services among many others), health diagnoses, and personal assistants.
For a conversational AI system to become any of the above, it needs to be fed the relevant datasets. If the chatbot at hand needs to support banking customers, it needs to understand the various processes customers perform and the issues the face. Conversational datasets that depict this help chatbots understand how to interact with such customers and it also trains them to solve customer queries and take action and responsibility.
A chatbot gets defined by the training data it consumes. It truly becomes what it eats. Chatbots are being adopted all across numerous areas of our lives, and results have shown that we like interacting with these intelligent beings. They make the interaction between people and organizations simpler. They enhance customer service and improve overall efficiency. But, building systems that can interact effectively with people brings about the need to learn how to be like us. That involves time taken to understand what it means to be human, and high-quality conversational datasets hold the answer to achieving that.