Artificial Intelligence in Financial Markets and Risk Management


Financial analysis, Portfolio Management and Investment advisory services need figures like ‘Beta’ and ‘Standard Deviation’ which reflect the risk of a particular stock. Mammoth institutional investors and big retail investors maintain highly diversified portfolios that reduce the overall risk of portfolio. This requires studying different past patterns of several stocks, their combinations and proportions in the portfolio to come up with an optimum portfolio. The more combinations we can see, the more optimum solution we can come up with.

Today AI has come up with complex algorithms and software that are made of concepts of statistics like Distribution function, Correlation and Regression Analysis and Value-at-Risk (VAR), etc. These software blend these concepts with the concepts of multivariable constrained optimization techniques which are completely packed with partial derivatives, Lagrange Multipliers, etc. The software that runs with the blend of all this mathematical logic will enable the user in prediction of movement, trend analysis and helps to come up with the optimized portfolio option. It also suggests real-time movements and gives an alarm whenever there is a need to change the composition of portfolio.

The user will have all the information on the screen. These real-time updates are made possible through machine learning process involving deep learning neural networking which makes the system able to do deductive reasoning, inference and decision making far better than humans. The machines learn and behave accordingly that reduces the error rate gradually.

Financial markets will become more volatile. The more efficient the software, the more it suggests accurately the fair price of the stock by reflecting all the information. This rapidly changes the pattern of holdings and orders are automatically placed with the broker.

These software also have link with the international markets and stock market news to give real-time information about the global news, currency and commodities prices which will enable the user to make quicker decisions.

The latest list of software includes software created by Vestserve serving portfolio management needs, hedging needs, etc., Riskalyze. Companies like Institutional Capital Network, Inc. have become one of the world top based investment advisory firms that are heavily dependent on these software which enabled them to grab a large client base.

Heavy investments are being made in the companies which are into the fintech services because of its scope for growth in the future. The financially naive can also go for trading in stocks with the help of suggestions given by this.

According to sources[1], global fintech market is going to reach about $300 billion by 2025. This is due to the high investment in technology-based solutions by banks and firms. The key fintech players would be Robinhood, Atom Bank, Microsoft, etc.

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Artificial Intelligence and Machine Learning changing the financial services industry?

Artificial Intelligence (AI) has become consolidate into our everyday lives. We can see it’s power in our social media newsfeeds, activates facial recognition (to unlock our smartphones), and even suggests music for us to listen to. Example: Alexa, Google Assistance

Machine Learning, a subdivision of AI, is progressively consolidated into our everyday and changing how we live and make decisions.

Machine Learning in finance

Business changes every minute, but more advancement in today’s technologies have accelerated the pace of changes. ML analyses historical data and it’s behaviors to predict patterns and make decisions. It has proved hugely successful in retail for its ability to custom products and services to customers. ML also distorts the insurance sector. Such as more connected devices provide deeper perception into customer behaviors, insurers are able to put premiums and make payout decisions based on data.


AI and ML have acquired popularity in the financing industry over the last few years. Large amounts of client data are federal into the AI and ML algorithms to evaluate credit quality and price loan contracts. These are some of the customer-focused uses of AI and ML in the financial sector:

Credit Evaluation and Scoring

ML tools intend for credit scoring speed up lending decisions and limit increase risk. Firms now depend on data not generally in their credit reports to perform credit quality analysis. Individual, unstructured, lenders have turned to additional, and semi-structured data sources such as social media activities to capture a subtlety view of creditworthiness. By applying ML algorithms to this group of new data qualify assessment of qualitative factors such as consumer behavior, and even willingness to pay.

Aside from this, the use of ML algorithms in credit scoring has led to greater access to credit. Traditional credit scoring models need a potential borrower to have enough amount of historical credit information to be considered scorable. Without these details, the model could not initiate a score leaving out possibly creditworthy borrowers without any means to build a credit history.

Appraise Insurance Policies

ML is used in the insurance industry to evaluate complex data, thus lowering cost and improve profitability.

AI and ML applications provide development/upgrade/improvement to the approval process and thus assist agents in categorizing through big datasets that insurance companies have collected to identify and separate cases that cause a higher risk, potentially reducing claims.

Furthermore, insurance companies can use ML to improve the marketing of insurance products through the incorporation of real-time highly granular data, such as online shopping behavior and telematics or pricing. Therefore, correct pricing of insurance could outstandingly change the insurance market as well as upgrade/enhance claims processing.


They are virtual assistants and help customers complete transactions and solve easy problems Automated programs use neuro-linguistic programming (NLP) to interconnect with clients either by text or voice. Chatbots are mostly used by financial firms in their mobile apps or social media. It provides answers to simple customer queries regarding the account balance, list of transactions, etc. Better communications with customers and Cost savings can both increase profitability.

Financial services are setting foot in the AI field and at varying stages of integrating it into their long-term organizational strategies.

Many companies have already started implementing intelligent solutions such as advanced analytics, process automation, robo advisors, and self-learning programs. But a lot more is yet to come as technologies evolve, democratize, and are put to innovative uses.

Embed AI in strategic plans

Integrating Artificial Intelligence (AI) into an organization’s strategic objectives has helped many frontrunners develop an enterprise-wide strategy for AI that various business segments can follow. The greater strategic importance accorded to AI is also leading to a higher level of investment by these leaders.

Apply AI to revenue and customer engagement opportunities

Most frontrunners have started exploring the use of AI for various revenue enhancements and client experience initiatives and have applied metrics to track their progress.

AI in Credit Risk Analysis

A recent study found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash[2]

Having good credit aids in receiving favorable financing options for any appliances, Clothes, offers over the market, landing jobs and renting an apartment, to name a few examples. Today is the credit card’s empire. Even business people are linking many offers with credit card payments which is making the use of credit card inevitable. Therefore, credit appraisal has become one of the most cardinal tasks of banks and financial institutions.

Artificial Intelligence solutions are helping banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process.

Quantitative Trading

Quantitative trading is the process of using large data sets to identify patterns that can be used to make strategic trades. Artificial Intelligence is especially useful in this type of trading.

AI-powered computers can analyze large, complex data sets faster and more efficiently than humans. The resulting algorithmic trading processes automate trades and save valuable time.

Personalized Banking

Traditional banking isn’t cutting it with today’s digital-savvy consumers.

A study conducted by Accenture on 33,000 banking customers gave results which show that about 54% want aid to help them to plan their budget and make real-time spending adjustments[2.] Besides, 41% are “very willing” to depend on the advice and help by computer banking[2].

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Cybersecurity & Fraud Detection

Numerous transactions will happen over a day because of money movements from one account to the other, online trading platforms, etc. which are needed to be regulated properly failing which will lead to unfathomable frauds and incorrigible effects.

It has become necessary for banks and financial institutions to pull up their socks for implementing cybersecurity and fraud detection efforts. 

Artificial Intelligence is playing a key role in improving the security of online finance.

Predicting cash movements and taking initiative of advising customers on their pattern of expenditure. ML helps in identifying suspicious movements in cash and thereby assists in preventing fraudulent transactions like in Money Laundering.


  1. infrastructure-report/ 

This article is co-authored with Ritu Parna Bagachi, BH VSN Manoj Varma, and Sri Venkata Sumanth Pisapati, Woxsen University.

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Professor Syed Hasan Jafar has over 10 Years of Experience in the field of Finance and worked as a Research Analyst and Corporate trainer. He comes on several national media channels as a financial expert for sharing his view on the financial market. His areas of expertise are Security Analysis, Corporate Finance, Equity, and Derivative Research and Wealth management. He completed his bachelor’s degree in Science from the University of Bangalore, his post-graduation Diploma in management from the Institute of Public Enterprise (IPE). He is NISM Certified Research Analyst. His areas of expertise are Security Analysis, Corporate Finance, Equity, and Derivative Research and Wealth management. He has conducted more than 50 Investor awareness programs across the country and has been awarded Best Research Analyst several times.