Advanced Data Analytics for the Banking, Financial Services, and Insurance (BFSI) Sector

Published by Cubic Author

Posted on 24/07/2024

Introduction to Data Analytics in the BFSI Sector

Now, the commercial benefits from big data and data analytics are no longer to be sold, and evidently, these opportunities have been reflected. The banking, financial services, and insurance companies have taken steps to set up new departments dedicated to big data analytics and have started recruiting focused professionals to carry out operations. In leading banks around the world, data analysis tools have already been sharpened and start operating. It’s no secret that big names like Citigroup, J.P. Morgan and U.S. Bank have already deployed data analytics tools to design their workflows, track customer behaviors, assess risks, and so on. These institutions have discovered that they have developed a 360-degree customer perspective using data analytics tools, segmenting different customers and giving a boost to the sales of personalized goods.

With globalization and a massive increase in purchasing power of the middle class, the banking, financial services, and insurance (BFSI) sector has never been in greater demand. The improvement in internet facilities and mobile technology has paved the way for digital banking, and the speed and ease with which one can access services online not only attracts new customers but also creates volumetric information. It is said that data centers and data hubs have contributed sufficiently to the massive growth in data. As high-speed internet and communication technology evolves, so too do the large data pipes that carry voluminous information suitable for businesses. Interestingly, with big data, data analytics techniques possess the ability to work on this set of large volumes of data and analyze it to predict market potentials or risks for BFSI businesses.

Key Techniques and Tools for Advanced Data Analytics in BFSI

Key Techniques in Advanced Data Analytics in BFSI The top techniques in advanced data analytics in banking, financial services, and insurance (BFSI) are as follows:

  1. descriptive analysis;
  2. big data predictive analytics;
  3. clustering analysis;
  4. machine learning techniques (artificial neural network, Bayesian classifier, K-nearest neighbor, support vector machine);
  5. textual data mining analysis;
  6. visualization;
  7. social media analytics;
  8. predictive modeling;
  9. time series analysis;
  10. classification tree;
  11. factorization model;
  12. ensemble models;
  13. long short term memory;
  14. sentiment analysis; and
  15. web analytics.

Each technique is discussed briefly in the next sections.

There are many techniques and tools that are used for advanced data analytics. This section provides details of the top tools and techniques used for advanced data analytics with respect to the banking, financial services, and insurance (BFSI) sector.

Applications of Data Analytics in Banking, Financial Services, and Insurance

Data and advanced analytics bring new opportunities to the banking, financial services, and insurance sector. According to a 2013 McKinsey report, the use of big data could lead to an additional $300 billion in value from the use of big data across US sectors: $120 billion in insurance, and $250 billion in personal banking. The tasks associated with BFSI analytics include customer insight, fraud and compliance, balance sheet management, risk management, business process, operations, distribution, and strategy. The objective is to gain enhanced insights, guide the firm’s decisions, become more responsive to the market’s characteristics, drive innovation, improve profit margins, and reduce expenses.

The banking, financial services, and insurance (BFSI) sector is an exceptional industry that has embraced next-generation technologies on a large scale. To name a few, advanced analytics, business intelligence, big data, artificial intelligence, data lakes, and robotic process automation (RPA) have been used in achieving agility through advanced metrics production, better decision making, personalized product offering for better customer experience, reducing frauds, and controlling the cost. In a technical sense, data analytics such as the descriptive, predictive, and prescriptive ones are the most widely category of techniques and tools that are being effectively used for increasing the business value. The motivation for BFSI firms in considering data analytics is visible across the areas of risk, the customer, operations, sales and marketing and compliance for better performances.

Challenges and Opportunities in Implementing Advanced Data Analytics in BFSI

In the process of evaluating and applying the tools to overcome this, we compare the performance of the tools in terms of their minimum coverage for the false negative alerts versus the time taken to classify the alerts. We compare Random Forest, k-Nearest Neighbors, Support Vector Machine, and Clustering techniques on both sparse and dense datasets and we come to the conclusion that simple k-Nearest neighbor algorithm and the Random Forest method outperforms the SVM and clustering algorithms. The best results scalability and feasibility performance-wise were obtained from the dense discrete k-Nearest Neighbors solution. We hope this is a practical guide for the development, deployment, and maintenance of AML compliance software. In the space of anti-money laundering (AML) and fraud detection in the banking space, we calculated the minimum coverage for the false negative alerts.

The BFSI sector is facing several challenges with processing and analyzing data to forecast future performance, meet regulatory compliance, and to detect suspicious behavior. To address the regulatory needs, one of the major global banks hired a thousand employees for a period of fifteen months for anti-money laundering (AML) compliance, spending a couple of billion dollars on compliance activities. Alerts coming from AML are vast and much of it is not pursued due to its high price. Many current software and hardware solutions are not well equipped to handle the data burst in the AML space. Many alerts are generated, and most of these are not suspicious transactions, wasting or misplacing valuable funds. In this chapter, we evaluate the scene of data analytics complimenting the BFSI sector and then go on to study the challenges and opportunities in implementing AML software. We break this into data classification and anomaly detection, and evaluate the different data science and machine learning techniques that are applicable to this kind of problem.

Future Trends and Innovations in Data Analytics for BFSI

As the main economic profit source for asset managers, the relationship marketing capabilities can enhance the process. Many fund managers and analysts are using third-party expiry warnings on the relevant IP addresses, taking technical host warnings that issue incoming reports and data fraud red flags seriously. The data collection for large datasets entails managed flow and quality control of the data, while the data engineering aspect focuses on adaptive models rather than just data mining. As a front line and risk management program, focus is shifting towards more advanced identification, and we begin to see progress on “modeling the adversary”. On this customer front, data analytics and multi-channel technology continue to be the primary drivers of market segmentation and innovation for the payment industry. The core focus across all leading practices for building communications capabilities was on using digital channels and leveraging advanced data analytics to allow for testing, learning, and personalizing communication. Content collaboration will leverage that in the future in three distinctive fashions. The urgency around planning and strategic growth has become multi-faceted, taking strategic changes beyond just design topics between benefit and brokerage plans and into point objectives.

The cloud promotes the integration and automation of data across systems and processes, enhancing processes such as cloud computing in governance, risk, and compliance (GRC). Another significant trend and innovation in the cloud revolution during the last few years was the rise of Business Process as a Service (BPaaS). The customer experience within data-aware industries like banking, healthcare, and insurance can be personalized by leveraging data analytics to enable the unified and comprehensive view of the customer across the company. The migration of banks into platforms will increase. The ability to execute to improve execution speeds and decrease execution costs is the primary objective of execution analytics and is a vital requirement for brokerage firms and banks. As an operational and risk management function, the operations centers of carriers need access to advanced data analysis for real-time operational monitoring. Established insurers attempting to become more advanced in their use of analytics can learn from the experiences of the progressive industry leaders across a range of criteria by leveraging leading practices of the advanced insurers. As instanced by these three examples, early technology innovators in the insurance sector are offering some creative uses for advanced data analysis.