How Data Science is Driving Efficiency and Impact in the BFSI Sector

The global market size for big data analytics in the BFSI sector is expected to exceed $86 billion by 2027. With the recent explosion in volume, velocity & variety of banking & financial data, the industry is compelled to extract insights from multiple complex sources using data science technologies to drive decision-making around improving sales revenues, customer experience (CX), etc.

In fact, in a sign of the growing importance of tech-led innovation in recent years, technology majors, including Google, Amazon, and Paytm, have forayed into the Banking & Financial Services industry and have leveraged their growing user base through bank alliances by developing payment and banking apps.

Data science is helping new-gen and established players in the BFSI industry overcome major data-related challenges. But what are these BFSI problems? Let’s discuss them first.

Among its focus points, data science enables banks to reduce their financial risks, mitigate their losses and manage their exposure.

Through data science-led risk management, BFSI companies extract many accurate insights. This can help them design their financial revenue models and use predictive indicators to improve growth.

Data science technologies combined with Machine Learning (ML) can also prevent financial fraud involving credit cards and financial accounting. Accurate data samplings help improve ML-enabled fraud detection with the power of data science.

Banks are also developing their customer support frameworks using data science, which provides all the sensitive customer data and financial investment patterns. This helps banks analyze which customers have sufficient credits (or not). Hence, it can offer the best financial products to customers based on their credit standing.

Another major challenge in the BFSI sector is acquiring and retaining customers. With growing competition in this industry, banks need a 360-degree view of their best customers to deliver personalized solutions and relevant care. This is essential to build customer loyalty and strengthen relationships. Customer lifetime value (CLV) is one predictive metric determining how long a customer will continue doing business with the bank.

Using high data volumes, banks can measure the CLV for each customer based on the following factors:

• Customer attrition
• Banking products & services that they are using
• The importance of their financial transactions
• Additional customer traits like demography, geographical location, and market
• Information

Data scientists and analysts in the BFSI sector also deploy techniques like clustering and decision trees to learn the CLV of different customer segments. This allows financial companies to allocate sufficient marketing resources for each customer group. How are data science technologies benefitting the BFSI sector? Let’s discuss that next.

Over the last decade, the banking industry has leveraged data science capabilities for a variety of business benefits.

Here are some of the major advantages:

Banks and financial institutions collect a growing volume of customer and financial data. Banks can use data science to detect customer behavior patterns from the gathered data. Banks can easily analyze their customer preferences and interactions using transactional data as digital and mobile banking gains popularity.

Thus, data science technologies can help understand customer sentiments and perceptions of the bank and explore new revenue-generating opportunities.

With more insights into individual customers, data science technologies enable banks to personalize their product offerings to customers based on their individual needs. Further, data analytics helps build customized ads on social media platforms most used by customers.

Additionally, banks can create customized offers for each customer based on their previous purchases. BFSI companies can also cross-sell complementary products and services to customers and generate automatic recommendations based on the customer’s life stage.

Data science helps banks, businesses, and governments detect cases of fraud around electronic financial transactions and insurance claims using specialized AI/ML-based analysis techniques such as cluster analysis, data mining, etc.

Also, it helps categorize their customer based on their probability of carrying out fraudulent activities so that banks can monitor the customer segment with higher fraud risk well ahead of time. Thus, it becomes crucial for financial institutions to implement predictive analytics platforms to get notified of anomalies in transactions faster and reduce the damaging effects of fraudulent accounts.

The financial services sector globally is challenged by significant risks around credit, market, security, compliance, liquidity, etc., apart from an unprecedented geopolitical and economic crisis.

BFSI organizations must adopt advanced, intuitive data science-backed solutions to gain more confidence in their risk management policies. Data analytics helps businesses reduce risks associated with accounting, business valuation, insurance claim assessments, large institutional investments, and various risk model validations.

With the help of data science technologies, BFSI companies can remain competitive in the fast-moving trading market. Data science tools provide a quick and efficient mode of analyzing traditional and non-traditional trading data that remains relevant only for a short time.

Financial companies can use real-time and predictive analytics to develop accurate statistical decision-making models. Additionally, they can use historical trading data to build algorithms for forecasting opportunities in the market.

Next, discuss how Emergys’s Data Science services can impact the BFSI sector.

Banks and financial services companies can only develop an effective data science solution with adequate technical expertise. At Emergys, we provide end-to-end services in Data Science, including identifying the right datasets, creating accurate algorithms, and integrating analytics into the organization’s operational processes for an accurate and deep understanding of the business.

Here are some of the services offered by Emergys as part of Data Science:

• Developing tailored data science and AI roadmap and analytics strategy for businesses
• Exploring financial data in a banking environment and determining the right use of data, model development and implementation
• Developing chatbots and text/speech-based assistants to automate communication within organization and with customers.
• Building on-premises or cloud-powered infrastructure for data science
• Exploring the numerous external and internal sources of data and building datasets
• Installing and configuring analytics/conversational AI platforms
• Developing customized algorithms and use cases.
• Developing proofs-of-concept for data science solutions

Our technology expertise in data science includes but is not limited to R programming language, Hadoop, Apache Spark, Python, DataRobot, Dataiku, Alteryx, ThoughtSpot, etc.