Introduction
In this data-driven world, Data Analytics has become vital in the decision-making processes in the Banking and Financial Services Industry. Investment banking and other businesses wherein, real-time information is used, volume, as well as the velocity of data, has become critical factors. Also, Data Analytics comes into the picture in cases like this when the sheer volume and size of the data is beyond the capability of traditional databases to collect.
Today, data analytics practices have made the monitoring and evaluation of vast amounts of client data including personal and security information by Banks and other financial organisations much simpler.
In this article, we will further stress down on the different areas in the BFSI domain where we use data sciences to reap huge benefits!
What is the BFSI sector?
BFSI is an acronym for Banking, Financial Services and Insurance and popular as an industry term for companies that provide a range of such products/services and is commonly used by IT/ITES/BPO companies and technical/professional services firms that manage data processing, application testing and software development activities in this domain. Banking may include core banking, retail, private, corporate, investment, cards and the like. Financial Services may include stock-broking, payment gateways, mutual funds etc. Insurance covers both life and non-life.
Data Sciences is allowing the BFSI industry to reach out to new markets and offer novel products and services through efficient delivery channels. Also, data security and availability of information updates is critical to the banking and insurance business, mandating high network uptime, rapid fault detection and quick problem resolution. The banking and financial industry is also challenged by a large number of existing legacy systems in its infrastructure.
Applications of Data Science in Banking Sector
Fraud detection
Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees. The sooner a bank detects fraud, the faster it can restrict account activity to minimize loses. By implementing a series of fraud detection schemes banks can achieve the necessary protection and avoid significant loses.
The key steps to fraud detection include:
- Obtaining data samplings for model estimation and preliminary testing
- Model estimation
- Testing stage and deployment.
Since every data set is different, each requires individual training and fine-tuning by data scientists. Transforming the deep theoretical knowledge into practical applications demands expertise in data-mining techniques, such as association, clustering, forecasting, and classification.
Lifetime value prediction
Customer lifetime value (CLV) is a prediction of all the value a business will derive from their entire relationship with a customer. The importance of this measure is growing fast, as it helps to create and sustain beneficial relationships with selected customers, therefore generating higher profitability and business growth.
Acquiring and retaining profitable customers is an ever-growing challenge for banks. As the competition is getting stronger, banks now need a 360-degree view of each customer to focus their resources efficiently. This is where the data science comes in. First, a large amount of data must be taken into account: such as notions of client’s acquisition and attrition, use of diverse banking products and services, their volume and profitability, as well as other client’s characteristics like geographical, demographic, and market data.
Customer segmentation
Customer segmentation means singling out the groups of customers based on either their behaviour (for behavioural segmentation) or specific characteristics (e.g. region, age, income for demographic segmentation). There is a whole bunch of techniques in data scientists’ arsenal such as clustering, decision trees, logistic regression, etc. and, as a result, they help to learn the CLV of every customer segment and discover high-value and low-value segments.
There is no need to prove that such segmentation of clients allows for the effective allocation of marketing resources and the maximization of the point-based approach to each client group as well as selling opportunities. Do not forget that customer segmentation is designed to improve customer service and help in loyalty and retention of customers, which is so necessary for the banking sector.
Applications of Data Science in the Financial Services Sector
Algorithmic trading
This area probably has the biggest impact from real-time analytics since every second is at stake here. Based on the most recent information from analyzing both traditional and non-traditional data, financial institutions can make real-time beneficial decisions. And because this data is often only valuable for a short time, being competitive in this sector means having the fastest methods of analyzing it.
Another prospective opens when combining real-time and predictive analytics in this area. It used to be a popular practice for financial companies have to hire mathematicians who can develop statistical models and use historical data to create trading algorithms that forecast market opportunities. However, today artificial intelligence offers techniques to make this process faster and what is especially important — constantly improving.
Robo-advisory
Robo-advisors are now commonplace in the financial domain. Currently, there are two major applications of machine learning in the advisory domain.
Portfolio management is an online wealth management service that uses algorithms and statistics to allocate, manage and optimize clients’ assets. Users enter their present financial assets and goals, say, saving a million dollars by the age of 50. A robot-advisor then allocates the current assets across investment opportunities based on the risk preferences and the desired goals.
Recommendation of financial products. Many online insurance services use robot-advisors to recommend personalized insurance plans to a particular user. Customers choose robot-advisors over personal financial advisors due to lower fees, as well as personalized and calibrated recommendations.
Applications of Data Science in Insurance Sector
Underwriting and credit scoring
Machine learning algorithms fit perfectly with the underwriting tasks that are so common in finance and insurance.
Data scientists train models on thousands of customer profiles with hundreds of data entries for each customer. A well-trained system can then perform the same underwriting and credit-scoring tasks in real-life environments. Such scoring engines help human employees work much faster and more accurately.
Banks and insurance companies have a large number of historical consumer data, so they can use these entries to train machine learning models. Alternatively, they can leverage datasets generated by large telecom or utility companies.
Auto Insurance
Picture a world in which wireless “telematics” devices transmit real-time driving data back to an insurance company. Now picture a bunch of auto insurers drooling over their desks.
Telematics-based insurance products have been around since 1998 when Progressive first launched them. But technology has come a long way in the intervening years. Telematics devices currently include embedded navigation systems (e.g., GM’s OnStar), on-board diagnostics (e.g., Progressive’s Snapshot) and smartphones.
These can be used to create personalized plans. In a SAS white paper, Telematics: How Big Data is Transforming the Auto Insurance Industry, the authors highlight two of these options:
- PAYD: Pay-As-You-Drive
- PHYD: Pay-How-You-Drive
PAYD is pretty straightforward. It charges customers based on the number of miles or kilometres driven. Hollard Insurance, a South African insurer, has six mileage options.
But PAYD does not take into account driving habits. PHYD plans use telematics to monitor a wide variety of factors — speed, acceleration, cornering, braking, lane changing, fuel consumption — as well as geo-location, date and time. If an accident occurs, the insurance company has the ability to recreate the situation.
Personalised marketing
The customers are always willing to get personalised services which would match their needs and lifestyle perfectly well. The insurance industry is not an exception in this case. The insurers face the challenge of assuring digital communication with their customers to meet these demands.
Highly personalised and relevant insurance experiences are assured with the help of the artificial intelligence and advanced analytics extracting the insights from a vast amount of the demographic data, preferences, interaction, behaviour, attitude, lifestyle details, interests, hobbies, etc. The consumers tend to look for personalised offers, policies, loyalty programs, recommendations, and options.
The platforms collect all the possible data to define the major customers` requirements. After that, they work on the hypothesis on what will work or won`t work. Here comes the turn to develop the suggestion or to choose the proper one to fit the specific customer, which can be achieved with the help of the selection and matching mechanisms.
Also, the personalisation of offers, policies, pricing, recommendations, and messages along with a constant loop of communication largely contribute to the rates of the insurance company.
BFSI companies using Data Science
BBVA Bancomer is collaborating with an alternative credit-scoring platform Destacame. The bank aims to increase credit access for customers with thin credit history in Latin America. Delta came accesses bill payment information from utility companies via open APIs. Using bill payment behaviour, Destacame produces a credit score for a customer and sends the result to the bank.
JPMorgan Chase launched a Contract Intelligence (COiN) platform that leverages Natural Language Processing, one of the machine learning techniques. The solution processes legal documents and extracts essential data from them. Manual review of 12,000 annual commercial credit agreements would typically take up around 360,000 labour hours. Whereas, machine learning allows to review the same number of contracts in just a few hours.
Wells Fargo uses an AI-driven chatbot through the Facebook Messenger platform to communicate with users and provide assistance with passwords and accounts.
Privatbank is a Ukrainian bank that implemented chatbot assistants across its mobile and web platforms. Chatbots sped up the resolution of general customer queries and allowed to decrease the number of human assistants.
Conclusion
The list of use cases in the Banking and Financial sector is growing day by day. The massive increase in the amount of data to be analysed and acted upon in the Banking and Financial Sector has made it essential to incorporate the implementation of Big Data Analytics. Knowing the importance of data science is crucial in these sectors and should be integrated into all decision-making processes based on actionable insights from customer data. Furthermore, Big Data is the next step in ensuring highly personalised and secure banking and financial services to improve customer satisfaction. In this extremely competitive market, it is essential for companies to invest heavily in Data Analytics.
