Implementation of Data Science in the Finance and Banking Sector

Posted by rohit rohi on September 8th, 2022

Data science continues to influence and shape how we think about problems across numerous industries as data has become more accessible thanks to technology. Predictive and prescriptive analytics have a wide range of applications in banking due to the data flow from point-of-sale transactions, deposits and withdrawals, customer profile (KYC), your own CRM, and other externally curated data sources. Data science may assist you in resolving various issues, regardless of where your firm stands. Today, we'll examine a few data science applications in the banking sector.

Application Of Data Science 

• Cross-Selling 

To satisfy the demands of their consumers, many banks provide a range of goods and services. These can include credit, investments, consumer and business loans, deposits, and more. Gaining a larger part of existing consumers' wallets depends on anticipating the goods and services they need. Modern data science techniques like predictive modeling can spot and foresee customer needs even before the customers are aware of them.

Predictive analytics pinpoint previous and present-day behavior in products and services that a consumer already subscribes to. Then, it adds to behavior other elements such as the customer's profile, credit standing, and more. In essence, the predictive model compares this one specific consumer to other comparable customers who took advantage of cross-selling possibilities in the past. The outcome is an actionable ranking of the likelihood that a customer will respond to the cross-selling of a product based on data science. This enables your team to prioritize marketing initiatives while efficiently concentrating on growing client relationships.

• Fraud Detection

The bottom line with your credit or debit card products is directly impacted by fraud detection, as is client trust and loyalty. The Federal Reserve claims that technological advancements and industry standards, such as the encapsulation of credit and debit cards with microchips, have significantly decreased point-of-sale thefts. However, as a response, thieves have started using your customers' credit or debit cards for online purchases. The rise in card-not-present fraud creates issues for banking since it makes it harder for customers to spot fraud at an early stage. The bank's anti-fraud activities are supported in large part by early detection.

Fraud detection modeling is the skill of accurately capturing the subtleties of customer behavior to separate suspected fraud incidents from your customers' routine daily activities. In order to assess the possibility that a purchase was out of the ordinary for any given consumer, fraud detection models, such as K-means clustering and deep learning models, organize specific information like the location of the purchase, the time of day, previous customer behaviors, and more. When used properly, these data science-based technologies protect banks and customers from annoyance, fraud, and significant financial loss. 

Visit the data science course to learn how data science techniques are executed for detecting fraud.

Management of Retention

Costs associated with keeping both clients and employees in the bank are incurred. Forbes claims that North American banks' annual customer churn rate is 11%. Understanding the characteristics that lead to turnover, or at the very least recognizing those who are most likely to quit early, can be extremely helpful in reducing the risk of attrition. Some banks use a set of sophisticated analytics technologies to give information that is specific to the experiences of their customers. Using Natural Language Processing (NLP) on call center logs as an illustration. In order to identify typical problems and the severity with which the bank can lose a customer, this procedure analyses transcripts in terms of keywords and sentiment.

You may discover and take action on clients who are likely to depart by using these advanced analytics tools to gather customer data in combination with understandable predictive modeling. In reality, your customer manager would have access to a list of clients who were most likely to discontinue doing business with you as well as a list of reasons why they were expected to do so (for example, the customer's most recent contact cited "Customer Service" as a problem). Employee retention might be based on the same ideas.

Competitive Marketing

The formula for effective marketing is the appropriate message, delivered at the right time through the right channel. Your marketing budget's likelihood of being converted is significantly decreased by failure on any one aspect. Building personalized messages for each potential customer entails personalized marketing. The message is crucial. For instance, it is a waste of money to promote loans for real estate listings to clients who lack the necessary resources. The right moment is also crucial. Does the customer plan to buy the house right away? If not, the bank can scale back its awareness marketing to maintain its effectiveness and prominence. The customer's level of trust, interaction, and intention to act on the advertisement depends on where you market to them.

Each of these three domains (message, timing, and channel) can be used by data science to refocus marketing budgets on possibilities with higher returns on investment. A/B testing can

 be used to test messaging to identify which commercials have the most significant ROI increases. You can determine when to prioritize advertising initiatives using trends and forecasting. Once more, A/B testing can inform you of the best channels to use to reach specific demographics in order to enhance conversion success.

Customer Lifetime Value (CLV)

A bank can prioritize marketing, concentrate efforts on communication, and decrease the attrition of essential clients by valuing consumers above and beyond their predicted return in the current year. The art of estimating a customer's projected return over a predetermined period of time is known as customer lifetime value (CLV). The goal is to assess the additional revenue that any customer will bring to the bank in the form of interest, service charges, and investments. CLV can be used as a statistic to assess long-term health and direct the creation of growth-oriented strategies.

Risk Analysis

Credit risk modeling is crucial when dealing with clients who could end up costing the bank money. However, analyzing client liquidity is not as straightforward as risk modeling. To start, comparing customers (ex., customers from different industries) is challenging. The last thing you want to do is to treat loyal consumers poorly and drive them away from your competition.

Explainable regression models can calculate a probability of default (POD) based on factors such as credit scores, banking habits, client attributes, and more. The probabilities are based on past customers' defaults. The model's capacity to be explained is especially helpful in letting credit officers or relationship managers know why one customer, who is identical to another, obtained a more dangerous score, which helps them plan their next course of action to lower the customer's risk.

There is no doubt that data science is playing a pivotal role in the ongoing change taking place inside the financial sector. Thus, data scientists and analysts will be beneficial for this industry. If you also wish to become a financial analyst or data scientist, an IBM-co-powered data science course in Mumbai is the right place for you.