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Role of Big Data and AI in Financial Trading

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.

Artificial Intelligence is the human intelligence programmed in machines. Machine learning, Deep learning, Natural language processing of AI enables recommendations, forecasts, reporting, and business analytics. AI builds intelligence from initial learning and continuous learning.

Big 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:

  1. Data Dependent: Both Big data and Artificial Intelligence need data that can benefit organizations
  2. 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.
  3. 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.

  1. Quantitative analysis and trading
  2. Trends and patterns in trading
  3. Trading opportunity analysis
  4. Minimize risks
  5. Increases accuracy
  6. Better trading decisions
  7. Market sentiments analysis
  8. 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 industry.

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.

Transaction Data: 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.

The 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.

  1. Automated Trading
  2. Fundamental Analysis
  3. Triggers

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.

Conclusion:

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.

Machine Learning and AI to cut down financial risks

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.

Artificial Intelligence

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 self-redress.

Machine Learning

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.

Financial Risks

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.

1. Market Risk

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.

Utilizations 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.

2. Credit Risk

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.

Use 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.

3. Liquidity Risk

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.

Application 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.

4. Operational Risk

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.

Application 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.

Financial Risks

Conclusion

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.

Role of Artificial Intelligence in Financial Analysis

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.

Types of Financial Analysis:

Types of Financial Analysis
  1. Cash Flow: It checks Operating Cash Flow, Free Cash Flow (FCF).
  2. Efficiency: Verify the asset management capabilities of the company via Asset turnover ratio, cash conversion ratio, and inventory turnover ratio.
  3. Growth: Year over year growth rate based on historical data
  4. Horizontal:  It is comparing several years of data to determine the growth rate.
  5. Leverage: Evaluating the company’s performance on the debt/equity ratio
  6. Liquidity: Using the balance sheet it finds net working capital, a current ratio
  7. Profitability: Income statement analysis to find gross and net margins
  8. Rates of Return: Risk to return ratios such as Return on Equity, Return on Assets, and Return on Invested Capital.
  9. Scenario & Sensitivity: Prediction through the worst-case and best-case scenarios
  10. Variance: It compares the actual result to the budget or the forecasts of the company
  11. Vertical Analysis: Income divided by revenues.
  12. Valuation: Cost Approach, Market Approach, or other methods of estimation.

Role of AI in Financial Analysis:

The finance industry is one of the major data collectors, users, and processors. Financial Services sector and its services are specialized and have to be precise.

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 Analysis?

AI implementation in Financial Analysis

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:

  • Automation
  • 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 AI in Financial Analysis

Advantages of Artificial Intelligence in Financial Analysis:

  1. 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.
  2. 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.
  3. 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.
  4. Creditworthiness & Lending: AI helps to process the loan applications, highlights risks associated, crosscheck the authenticity of the applicant’s information, their outstanding debts, etc.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.

Conclusion:

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 finance sector.