The global market size for big data analytics in 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.

How Data Science Is Solving Business Problems in the BFSI Sector

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 a wealth of accurate insights. This can help them design their financial revenue models and use predictive indicators to improve growth.

Data science technologies combined with technologies like 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 that determines how long a customer will continue to do 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 volume of their financial transactions
  • Additional customer traits like demography, geographical location, and market

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:

1. Managing Customer Data

Banks and financial institutions collect a growing volume of customer and financial data. Using data science, banks can detect their customer behavior patterns from the gathered data. As digital and mobile banking gains more popularity, banks can easily analyze their customer preferences and interactions using transactional data. Thus, data science technologies can help understand customer sentiments & perception of the bank, and explore new revenue-generating opportunities.

2. Personalized Marketing

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

3. Fraud Detection

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

Also, it helps categorize their customer base 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 & reduce the damaging effects of fraudulent accounts.

4. Risk Management

Financial services sector across the globe is challenged by significant risks around credit, market, security, compliance, liquidity, etc., apart from an unprecedented geopolitical and economic crisis. BFSI organizations need to 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.

5. Algorithmic Trading

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 both traditional and non-traditional trading data that remains relevant only for a short period of time. Using real-time and predictive analytics, financial companies can develop accurate statistical models for decision making. Additionally, they can use historical trading data to develop algorithms for forecasting opportunities in the market.

Next, let us discuss how Ellicium’s Data Science services can create an impact in the BFSI sector.

How Ellicium’s Data Science Services Can Help Businesses

Without adequate technical expertise, banks and financial services companies cannot develop an effective data science solution. At Ellicium, we provide end-to-end services in Data Science, including identifying the right datasets, creating accurate algorithms, and integrating analytics into organization’s operational processes for accurate and deep understanding of the business.

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

  • Developing tailored data science & AI roadmap and analytics strategy for businesses
  • Exploring financial data in banking environment and determining the right use of data, model development & 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 & internal sources of data and building datasets
  • Installing, 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.


Data science has become critical for the banking and financial services industry to extract data-driven insights for impactful decision making. It must be integrated into all the major business processes in order for organizations to inspire innovation and exceed customer expectations.

At Ellicium Solutions, we enable business transformation through our portfolio of services including Business Intelligence & Data Visualization, Data Management, Data Platforms & Big Data, Data Science – Artificial Intelligence (AI)/Machine Learning (ML), Cloud Data Services, Robotic Process Automation (RPA) and Managed Services. Learn more about our expertise through our customer success stories. Read our blog on 5 reasons to work with a Data Lake managed services partner like Ellicium. Get started on your data transformation journey.
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