Business is all about making money, and that includes the business of transferring money. Money transfer companies (MTCs) or international remittances companies provide multiple options for customers (individuals and companies) to remit income and other expenses nationally and internationally.
MTCs provide online and offline payment solutions. Offline solutions refer to physical stores located across cities internationally. Using these stores, customers can provide physical cash and request their delivery in another currency to a store closest to the recipient. Offline stores are the most traditional options for overseas money transfer. Such stores are generally located in most cities across the world, especially in airports. Online payment solutions include online money transfer options such as bank transfer, credit/debit card, sofort, ACH direct debit, etc. This is an easier and more convenient alternative for customers who are savvy with internet banking options.
So, how do such businesses make their money?
Foreign exchange companies make their money from the charges levied on each transaction. Transfer fees represent a bulk of a money transfer company’s profits, and charging such fees allows them to increase their bottom line.
The transfer fees are determined based on several important money transfer parameters, the primary ones being transfer amount, transfer speed, and transfer rate.
Transfer account – The amount of money transferred in a single transaction
Transfer speed – The time taken in seconds, hours, or days, for the money to reach the recipient.
Transfer rate – The exchange rate for the selected send and receive currencies
Competitors across the money transfer space function within the same parameters, thus making it tricky to stand out. Some businesses are focusing more on offline stores while some others are directing their efforts into advanced online payment solutions. Irrespective of such business plans, every money transfer company can utilize competitor intelligence to optimize its parameters and increase revenue.
Competitor Intelligence and its advantages
Investopedia defines competitor intelligence as the ability to gather, analyze, and use information collected on competitors, customers, and other market factors that contribute to a business’s competitive advantage.
In the money transfer space, access to competitor intelligence such as their transfer fees levied for selected send amounts, payment options, and delivery speed can be analyzed to understand where such businesses are profiting. Using such numbers, you can optimize your offerings, thus improving customer satisfaction and increasing your bottom line.
Competitor intelligence in the form of pricing datasets allows you to track your competition, study competitor reaction to pricing changes, and explore untapped markets. It also helps you understand which aspect of your business operations needs to be improved to stay on top of the foreign exchange game!
Uses of competitor intelligence
Some of the popular use cases include:
Identifying competitors that offer the best deals to their customers
Fees charged by competitors across all currency corridors
The total premium charged by each competitor on all currency pairs
Time to disbursement vs premium charged
Bridged offers competitor intelligence solutions to money transfer companies looking to conquer the forex market. Click here to access our free pricing datasets, and let’s figure out a solution that gets you to the top!
One of the most exciting industries that AI has influenced is banking and finance. AI developers have identified multiple use cases for automation and machine learning in financial services and money transfer operations.
The financial services industry is all about making good financial decisions. Be it the bank or the customer, everyone wants to make the best bang for their buck. With the advancement in business intelligence and data science, we now know that the answers to all our questions lie in clean, consolidated, and insight-offering data. For financial institutions to gain an advantage today, they need to leverage the power of data, something that’s often called the lifeblood of Artificial Intelligence and Machine Learning models.
In the past few years, Artificial Intelligence has shown how it can revolutionize virtually every industry. Here’s how AI will continue to influence the finance space:
Artificial Intelligence has paved the way for a higher emphasis on customer satisfaction. With user data, banks can understand the type of customer they’re catering to, and suggest personalized services accordingly.
Banks collect user data from customer transactions, subscriptions, investment plans, etc. Today, banks can acquire even more information using chatbots. Banking chatbots allow customers to voice their concerns and receive immediate attention. Banks can collect such queries from multiple customers for gaining insights. For example, if many customers complain about not being able to perform a certain type of online transaction, banks can decide to make the process simpler by optimizing their webpage/user interface.
Such chatbots can also perform quick transactions, provide balance sheets, etc.
Personalized banking can also help customers achieve their financial goals. Based on a customer’s financial information, banks can provide personalized advice and an objective route to monetary success. Personalized services such as bill payment reminders, expense planning, etc. can go a long way in increasing customer satisfaction and brand loyalty.
Credit scoring makes lending decisions simpler for banks and money lenders. Such scores are determined using data representing your payment history, current debt, credit length, new credit, and credit types. Each of these data points hold different weights while determining a customer’s credit score.
Today with Artificial Intelligence, the credit scoring system can benefit from higher objectivity. AI models provide a more accurate representation of a customer that wants to borrow money, all at a far lesser cost. AI-backed credit scoring processes can function on a higher level and a more complex set of rules. Using such a credit scoring system, banks and money lenders will have a better understanding of how much money can be made of money lent. With AI-backed credit scoring, banks can distinguish between applicants that perform regular late payments and customers that pay bills on time. Such a credit scoring system can also provide a more accurate score to young applicants who do not have a long history of borrowing money.
Markets fluctuate and economies rise and fall. These behaviors are influenced by factors such as the sub-prime mortgage crisis (that led to the 2008 financial crisis), and the coronavirus outbreak that is causing the on-going 2020 stock market crash.
Machine Learning algorithms can learn from historical data and identify patterns. Such systems can locate economic threats at an early stage and create a call for mitigation. This will ensure that the financial mistakes of the past never happen again. AI/ML models can perform high-level market analysis that would take ages for humans to perform. As a result, the machines can alarm us about potential issues the economy could face, thus giving us the time and the data resources to tackle it.
Another important activity in banking is locating financial fraudsters. As mentioned earlier, AI systems that can assign highly representative and accurate credit scores can also detect users known for fraudulent activities. AI-backed fraud detection systems analyze a customer’s transactions and purchasing habits. If AI systems detect unusual activities (such as the sudden withdrawal of an unusually large sum of money/expensive purchases unlike regular activity), they can trigger the required security mechanisms, thus saving the customer from being de-frauded.
We have witnessed an increase in the number of data-driven investments in the past couple of years. With data, everyone has an objective reason to make a decision, and this includes trading on the stock market.
Also known as algorithmic, quantitative, or high-frequency trading, it has allowed for more fact-based reasoning behind investing. AI models view data with ultimate objectivity, and human flaws such as confirmation bias don’t influence AI models. So, when an AI model suggests a certain lucrative investment, you can rest assured that the suggestion is based on exhaustive stock market research.
Trading floors also benefit from AI-inspired solutions since such systems save time, and we all know that time is money.
While dealing with money, we can’t compromise on accuracy. Artificial Intelligence provides that very solution to the financial services industry. Future predictions for AI in finance show the industry is rapidly changing, and processes are being revolutionized left, right, and center.
From AI-backed credit scoring to personalized banking, Artificial Intelligence has forced every major banking institution to become a tech company. Interestingly, Goldman Sachs, one of the world’s largest investment banking enterprises, employs more software programmers and tech engineers than Facebook! It’s a growing sign of the future of services in finance, who’s “a” today stands for none other AI.
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.
Considering the recent development of AI / ML, it is worth exploring the role of Big Data and AI in revolutionizing financial trading. Internet accessibility, mobile smartphones, social media platforms increase the information exchange. Financial trading is complicated, requires complex calculations that use formulas and other factors that affect are market influencers. Thus the trading for a common man is challenging.
In 2018, the global trade finance market was valued at $ 59,500 million. It is expected to touch the mark of $ 71,000 million by the end of the year 2024.
In 2016, the International Data Corporation (IDC) had predicted that sales of solutions based on big data analytics would reach $187 billion by the year 2019.
What is Big Data & Artificial Intelligence?
Big data is voluminous data in either raw or structured form collected from various sources by the organizations. This data is important for businesses but the processing is complex. It requires technology-based solutions to clean, format, manage data and make it usable. It helps in improving operations and make decisions faster than before due to the insights available.
data has an input of raw data and AI pulls input from Big Data. The Big data is
the initialization of data processing and AI is the output that can help you to
make better business decisions.
Define Relationship between Big Data and AI:
Data Dependent: Both Big data and Artificial Intelligence need data that can benefit organizations
Accurate Predictions: Insights are precise with AI to support Big Data, which is just a collection of data. Manually it is impossible to find sense out eg. Big Data but AI can speed the process to highlight actionable.
Trading performance: Big Data has a detailed track record of each trade, broker, trading company and stock. AI empowers us to utilize this gathered information to draw promising results.
What is Financial Trading?
Financial trading is buying and selling of stocks, bonds, commodities, currencies, derivatives, and securities. The price of a financial instrument is determined by demand and supply. Factors that affect financial trading are market conditions, economic conditions, and market influencers. The process of trading is shortlisting financial instruments, buying or selling via broking houses or online trading platforms.
Benefits of Big Data and AI in Financial Trading:
We no more rely on human intuition, knowledge and data-based
decision-making gained importance with the development of technology.
Quantitative analysis and trading
Trends and patterns in trading
Trading opportunity analysis
Better trading decisions
Market sentiments analysis
Financial market analysis
Revolution in Financial Trading by AI and Big Data:
Each step of financial trading cycle is crucial and the technology can increase the profitability or at least the probability of success. Changes in the financial market are faster than a blink of an eye and at times stagnant. This dynamic or sluggish behavior of the market can tempt traders to take actions out of impatience. This is where advanced technologies play a vital role.
How big data and AI has revolutionized financial trading?
The massive data stored is formatted to benefit data analysis
and analytics. AI discloses valuable insights from the data pertaining to the
Intelligent algorithms designed using Big Data and Artificial
Intelligence can help us accomplish our financial trading goals.
Distinct information about the trading patterns, market trends, market reviews, and potential trades is possible due to Big Data. AI can predict using this data stored for trading patterns, market trends, etc.
The growth of Big Data leads to better AI solutions. It can encompass more data to learn from and analyze. A combination of AI and Big Data will be in demand as people have started tasting the fruits of this technology. Their interdependencies provide interesting results. AI brings reasoning power, automates learning and allows scheduling tasks relating to financial trading.
Measurable Trading Growth: Financial trading with AI technology-based algorithms will foresee quantitative trading. Growth in the number of traders and trading activities is the result of data-driven intelligent trading systems. Quality data, proper processing and connecting it with applications facilitate users in prompt decision-making. Programs and AI tools have left aside the manual trading strategies that once prevailed. Accurate outcomes are one of the major reasons for using Big data and AI in financial trading.
Offerings: Various applications that AI introduced to the field of financial trading are systems that recommend stocks, an investment able period, and signals buying and selling. Predict price movements, annual returns, link current world affairs and its impact on the markets. It can even help in portfolio management. It can predict new investment models and introduce profitable algorithms.
Reliance: Customers can rely on the mechanisms developed to meet the financial goals of long term and short term. Secured transacting and faster dealings increments the transactions to prevent frauds and meets the requirements of financial market compliances. Surveillance of trading platforms by the stock exchange includes the micro-level check on the technological tools that can disrupt the process.
Bots advisory services: The chatbots assist users in making financial decisions
keeping customer preferences in mind. Suggestions and solutions presented by
them are free of bias and does not manipulate humans. The time, energy and
costs involved are lesser compared to the human agents that provide service.
Risk Mitigation: Human errors and manual processing issues are diminishing with the new technology financial trading implemented. Big data and AI improved the trading process right from reviewing stocks, placing an order, execution of the order, and delivery. We can schedule notifications, information, and confirmations using AI. Fraud detected is analyzed by the exchanges and take corrective measures or levy penalties on the fraudulent parties.
Sentiment Analysis: Evaluating market sentiments requires opinion mining from sources like social media posts, blogs, articles, etc. This huge data processing uses advanced data mining tools to produce a summary of performance on stocks and commodities and influencing market trends.
Enrichment of transactional data can help customers monitor the stocks, current
prices, futuristic price, and trade better. This data shapes up as historic
data after a while and the accuracy of this matter in creating efficient
algorithms for financial trading.
Market Predictions: There are no complete predictive solutions in financial trading. The tools that AI provides can convincingly improve the trading abilities, reduce the chances of loss-making, and track the market movements. If, in case 100% accuracy is achievable in predicting the markets the trades will never accomplish. The situation of no profit and no loss-cannot be ideal for any business. A market prediction in this industry is its volatility and stability probabilities. Precautionary actions based on predictions or safe trading as a practice can help traders and investors.
future of financial trading with Artificial Intelligence:
Secured trading is a result of the numerous calculations that AI performs in negligible time. Absolutely eradicating the past methods is possible when current solutions are effective. AI performs operational transactions, enables high- frequency trades, highlights unprofitable transactions, and most important is it keeps learning to improve.
The drawback is that we just cannot predict future prices based on historic data; hence at least partial automation is possible. AI can assist in creating a trading account and completing the account opening procedure, send a welcome kit, and introduce the user to trading with training videos.
The trading strategy created and modified with the help of technology scans data and market patterns. It helps predict intraday price movements and recommends trading actions. Queries are resolved and responded accurately based on historic data AI inspects. Intelligent search platforms and tools generate valuable insights based on market behavior to improving trading.
The finance sector is full of opportunities for investors and companies. If we implement Big Data and Artificial Intelligence technology in several fields, the difference in results is noticeable. Execute large trading orders in single or multiple groups using AI. Scheduled trading can save time and efforts of human beings. The trade operations are AI automated, they can control activities that are of repetitive nature for each trade that takes place. Manage the calculations, processing of receivables and payables, account balance, stock holdings.
AI can help finance sector and financial trading activities to provide customer service 24×7. It can process settlements, resolve basic level issues, and share the latest updates to the customers. Investing decisions if AI-supported can benefit the user and it can act as the main investment qualifier for the preferences set by them. Observe the stock performance risks and set targets for the risk capacities we hold or price to profit levels.
Big data and Artificial Intelligence are almost inseparable, especially with their unique abilities that help businesses. Like knowledge is available everywhere the advantages of Big Data and AI are widespread. The established facts that the finance industry uses this technology extensively is enough to draw advantages and having a competitive edge over others. Humans along with machine help can lead a better financial life.
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