Role of AI & Data Analytics in Finance

Summary: The role of AI & data analytics in Finance is increasing by leaps and bounds. Every other day we get to read a new research paper about a machine learning algorithm or a new library for a programming language. Earlier, advances in AI didn’t get to see the light of the day. However, today, even minuscule improvements are talked about in the media. Some of the best examples that show the rapid developments in AI are Deep Learning algorithms developing realistic human faces, AlphaGO defeating the world champion at GO, and Deep Fakes that consists of images or videos displaying people in hypothetical situations. 

AI has the potential to disrupt every industry. For example, the recommendations on Netflix and discount coupons for online shops on emails, to name a few. AI allows businesses to gain the upper hand in the market in the following ways:

• Collect data from multiple sources that help make better decisions.
• Minimize customer churn by identifying hesitating customers.
• Automate repetitive and manual tasks, allowing employees to focus on meaningful jobs. 
• Create beneficial target segments and provide productive recommendations to customers.

This AI revolution is also impacting the finance industry. Forbes reports that about 70% of all financial services firms depend on machine learning algorithms to fine-tune credit scores, predict cash flow events, and detect fraud. 

This article will discuss how AI & data analytics in Finance significantly impact the industry. 

Applications of AI & data analytics in Finance

Here are a few areas where AI & data analytics in Finance are making rapid strides by increasing efficiency and productivity over traditional approaches.

1. Credit scoring

Banks, fintech companies, and other financial corporations often lend money to people. Therefore, they need to examine the creditworthiness of the borrower accurately. 

Traditionally, analysts made such decisions after conducting several interviews with the borrower — individual or company — and collecting essential data points. But AI & data analytics in Finance allows for faster and more accurate analysis of the potential borrower, using complicated algorithms that are significantly better than the manual scoring systems of the past. 

AI uses the following parameters to judge the creditworthiness of the borrower:

• Geographic
• Demographic
• Psychographic
• Behavioral
• Past purchasing pattern
• Past credit history
• Income
• Savings

An added advantage of using AI & data analytics in Finance is that the scoring system is unbiased because it’s devoid of human intervention.

2. Fraud prevention

Fraudulent activities such as money laundering, phishing scams, and credit card fraud are proliferating due to the increased exposure of eCommerce portals, rising online transactions, and third-party integrations. 

A recent report by Nielson shows that card-based payment systems led to a loss of USD 28.65 billion in 2019.

Earlier, organizations used to follow a set of hardcoded rules to fight against the menace of fraudulent transactions. But in most cases, the fraudsters used to learn about these rules and then exploit the system. Fortunately, AI-based solutions evolve with time by finding new trends and patterns in the data. Moreover, modern machine learning algorithms are adept in anomaly detection. They can analyze multiple transaction-related features of a customer, including buying pattern, behavior, and location, to recognize if something is out of order. 

AI & data analytics in Finance have already proved their mettle through traditional machine learning algorithms such as support vector machines, logistic regression, and decision trees. But thanks to high-level algorithms like Deep Neural Networks, which can identify patterns in unstructured data, organizations can spot imposters and reduce fraudulent transactions. 

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3. Robo-advisory

General observation shows that savings account are no longer giving valuable returns. Therefore, people are moving towards passive investing that generates good returns over a long period. Robo-advisors are AI programs that suggest portfolio recommendations with the potential to create massive returns based on the investors’ individual goals, disposable income, and risk preferences. Customers only need to invest a particular amount each month — manually or automatically through the robo-advisor — after which the AI application handles everything from selecting the best assets to invest, buying them, and rebalancing the portfolio after a particular interval. 

The most significant benefit of Robo-advisors is that customers don’t need expert financial knowledge for using them. Moreover, they are an inexpensive but better alternative to human asset managers. 

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4. Algorithmic trading

The stock market is all about quickly identifying and analyzing the changing market trends and then using them to make better trades. Without AI & data analytics in Finance, it’s hard to detect the changes in the trends, and even if you do, you miss the time and the opportunity vanishes. 

Financial organizations are investing in algorithmic trading at an increased pace because they can make split-second decisions and execute numerous trades simultaneously. Such AI applications significantly outperform human traders in speed and accuracy, and most importantly, they aren’t affected by emotions. 

Mordor Intelligence reports that AI-supported systems handled anywhere between 60-73% of all equity trades in the US. Algorithmic trading uses a combination of machine learning and deep learning algorithms to forecast asset returns reasonably well over a given period. Another area where algorithmic trading applications excel is accessing multiple information sources, including satellite images, and using Natural Language Processing (NLP) for identifying exact sentiments from news articles, Reddit, Twitter, and much more. 

Freelance traders use algorithmic trading to build indigenous trading systems for picking the best stocks and executing trades when the best opportunity shows up. 

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5. Personalized banking experience

Banks of all sizes are trying their best to provide a personalized banking experience to customers. For example, chatbots offer instant solutions to customer queries. Using Natural Language Processing (NLP) techniques, chatbots can understand the customer’s intent and answer common questions like checking current bank balance, changing current password, scheduling transactions, and much more. 

The best part is that chatbots can adjust their responses based on customers’ emotional states. For example, suppose the customer wants to get an immediate response and avoid waiting to hear from a human consultant. In that case, the chatbot can provide a pre-designed readymade solution. On the other hand, if the customer is angry, the chatbot will transfer the call to a human consultant who can calm down the customer by providing thoughtful responses. Chatbots are gaining tremendous importance in the financial world because of their ability to handle multiple customers simultaneously in a cost-effective way.

However, chatbots aren’t the only AI applications that provide a personalized experience in the world of Finance. For example, several banks and fintech companies offer tailored financial advice, such as tips to cut monthly expenses that help increase savings, after analyzing the customers’ buying patterns and spending habits. It also includes sending automated alerts to customers regarding upcoming expenses, recurring transfers, and investments to ensure customers make wise financial decisions.

6. Process Automation

AI & data analytics in Finance offers various automatic solutions that reduce the dependence on the workforce. For example, by using advanced Optical Character Recognition (OCR), banks can automate manual and repetitive tasks that employees otherwise handled. Some examples include processing forms, creating digital copies of documents, and extracting crucial data from documents. 

Many financial organizations use dedicated software solutions or build indigenous solutions for the Know Your Customer (KYC) process. They make easy-to-use systems that allow authenticating ID to avoid fraud. For example, customers can take a selfie from their smartphones to match against the ID. The AI software working in the background will check whether the ID is fake and also find disputable content. Fortunately, Convolutional Neural Networks (CNNs) display excellent results in this area.