Big Data in Finance

In this article we'll be covering :

  • The role of Big Data in Finance
  • Some use cases of Big Data in BFSI
  • Challenges faced by Banks
  • The right tools for analytics in BFSI
  • A Course5 case study
  • How you can get started
Updated on
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Big Data Analytics for Banks and Financial Institutions

The Banking and Financial Services and Insurance industry (BFSI) is considered as one of the early adopters of analytics. With the increasing adoption of IoT devices and the unprecedented rise of Big Data, the BFSI industry has been reinventing itself to keep pace. Additionally, factors such as rise in operational costs, cutting edge competition, and incremental risk are driving banks and other financial institutes to constantly innovate and differentiate.

This article is comprehensive study of the evolving role and importance of Big Data in finance, and how it is changing the BFSI industry forever.

Big Data in Finance

Banking has always been considered a data heavy industry, thus analytics has the ability to redefine the playing field through data-driven decision making. Analytics solutions can help businesses across different horizontals of the organization ranging from Marketing and Sales, Operations to HR management. A data-driven and evidence-based business model allows banks to better understand their customers, markets, competitors, and to better empower the workforce.

There have always been solutions that can help companies achieve analytics excellence. What has changed is the scope of their applications which have been driven by the volume, variety, velocity and veracity of the incoming data. And also, by deviations in the regulatory and economic landscape. Analysts are expected to navigate these platforms and monitor vast volumes of data, identify market trends and customer behavior patterns, for developing effective strategies. And the value of the data is to a great extent dependent on how it is collected, stored, analyzed, and interpreted.

To seamlessly integrate legacy systems with new data architecture, analysts are adopting cloud-based solutions to support their data analytics needs. Cloud solutions not only help the enterprise save costs of maintaining and operating on-premise hardware, but seamlessly integrate unstructured and siloed data across business functions without compromising on data security.

Analytics consulting services are helping banks and financial institutions make data-backed decisions to improve their business processes, customer targeting, and customer service while mitigating risks, and preventing fraud.

Related Article: Trends in analytics

Use Cases of Big Data in Finance

Big Data analytics platforms in combination with intelligent modeling techniques, can really push the BFSI industry to new heights. Effective tools offer varied benefits across functions such as Risk, Marketing and CRM to:

  • Focus on customer-centric approach Monitor compliance effectively
  • Take faster and more data-driven decisions thus increasing operational efficiency which in turn contributes to the overall revenue enhancement
  • Compute various scorecards such as collection, application to better risk management
  • Identify key products and customers
  • Allow senior management to obtain competitive insights to build into their overall strategy

Let’s take a closer look at some of the key use cases facilitated by Big Data in the financial industry.

Customer Journey Analytics:

Customers interact with BFSI companies multiple times in a day, at various offline and online touchpoints, generating cross-platform data. With the right data architecture and data modeling expertise, it is possible to resolve, match, and stitch this “big data” across multiple channels. A clear customer journey mapping helps financial institutions to identify points of friction for customers, and gives a full view of the customer experience.

Example: Course5 was able to carry out Customer Journey Analytics for a prominent player in the BFSI sector in U.K. and the U.S.A., enabling their digital marketing team to optimize content for targeted marketing. By gaining a deeper understanding of customer behavior, the client was able to deliver were a seamless customer experience across touchpoints. This also helped us to enhance advocacy of both the brand and its products.

Customer Segmentation and Targeted Marketing

Customer Segmentation Analysis examines data on existing and potential customers, identifying groups based on common factors such as preferences, purchase patterns, socio-economic status, and their geographic location. A better knowledge of customers, helps marketing teams to craft customized campaigns targeting specific customer segments. Clients such as commercial and retail banks, are often looking to analyze historical data for insights on customer recency, frequency, and the monetary value of customer interactions.

Example: Course5 was able to help such a client based out of the UAE, to segment its customers into homogeneous groups, and set up a RAD framework based on risk, potential, and profitability. We delivered actionable insights on the potential for customer acquisition, retention, and growth, enabling the client to boost ROI and keep risk factors under observation.

Predictive Analytics and Future Planning

Predictive analytics falls under advanced analytics capabilities, where automation and ML models analyze historical data to predict market trends and customer behavior. It helps determine the Next Best Offer (NBO) for concerned products, and to identify opportunities for growth and associated risks.

Example: Course5’s AI model for NBO helped a middle eastern commercial and retail bank, predict customer preference related to products, and offered recommendations based on the probability of conversion. The insights enabled the client to identify product holdings and develop curated promotional content for the respective demographics.

Risk Assessment and Management

Financial companies have to carry out risk assessment and mitigation when engaging in any significant decision-making concerning investments and loans. AL and ML algorithms are enabling predictive and prescriptive analytics, minimizing human labor and human error, while delivering real-time insights through automation. The platforms also take into consideration all factors affecting the decision, from the state of the economy and market trends, to customer segmentation and customer behavior. Customer-facing personnel at banks can instantly check credit history if they are issuing loans, and not have to wait for approvals to get processed.

Fraud Detection and Prevention

Big Data Analytics solutions for BFSI are helping companies track customer engagement, behavior, and spending patterns, all in real-time. In case of stolen credit/debit cards and other cybercrime, the analytics solution can identify fraudulent activity, accentuate the insights with geolocations and related data points, and prevent both the customer and the institution from suffering major losses.

Example: Course5 developed a fraud detection model for large private banks in the APAC regions which were reportedly suffering massive losses due to fraudulent transactions on credit cards. The model proved to be extremely effective, identifying fraudulent transactions with an accuracy of 99.87%. The model facilitated rapid and accurate detection, enabling the client to make better decisions and prevent major losses.

Financial Markets and Investment Analysis

Insights gained from financial market analysis are a goldmine to firms within the industry, who now have hard data to substantiate their investments in companies, commodities and stocks. The algorithms do not simply analyze share prices, but also account for socio-political trends and disruptions. Predictive analytics can identify market trends and fluctuations in share prices, enabling investment companies to make data-backed decisions while dealing in stocks. In a highly competitive environment, being able to access real-time insights not only benefits decision-making for present investments, but long-term strategies for managing past, current and future investments.

Big Data analytics in banking offers several advantages can enable you to analyze your enterprise’s financial performance, and foster growth in individual business units as well the organization as a whole. But there are always some affiliated challenges you need to address.

Challenges of Big Data in Banking

The rapid evolution of technology and the adoption of IoT devices has led to a massive surge in Big Data. Legacy systems are becoming increasingly incapable of handling the volume, variety, veracity, and velocity of the data influx. Data management is technology dependent, and you have access to powerful tools that can help manage your data and extract actionable intelligence.

Financial institutions have to now figure out how the analytics tools are going to integrate into their existing systems, aligning business initiatives based on data-led initiatives, and bringing about organizational change. They also need to recognize the challenges specific to Big Data analytics in banking, because it’s a complex industry with sensitive data.

Challenges of Big Data in Banking

  • Data Security
    Hackers and malware are two of the primary threats to data security in the financial institutions. Cloud-based analytics platforms help protect sensitive data. Any fraudulent or suspicious activity is immediately flagged and mitigated.
  • Data Privacy Regulations
    There are strict regulations in place for banks and financial institutions handling critical data. They not only regulate the access to data, but require constant reporting to ensure due process. Data analytics platforms are making it simpler to manage financial data, upscale analytics initiatives cost effectively, and leverage enhanced processing power to access critical insights.
  • Data Silos
    Data is constantly coming in from various personal sources such as emails and employee documents, and from every interaction people have with your enterprise across channels. All the data gets put into silos for simpler management. But it is simple so long as there is an effective tool for integrating all the data, analyzing it and extracting relevant insights. Otherwise it becomes a stockpile of data that you have no clue how to address or where to even begin. Cloud-based tools are also an advantage you should be leveraging to improve your data silos and have quicker access to insights.
  • Data Quality
    Last but not the least, the quality of data that you get to work with, actively determines the quality insights you base your decisions on. The data being presented by your analytics solution needs to be safe, accurate, and actionable. Only then can you confidently take data-backed decisions that will positively impact your enterprise.

Getting Started with Data Analytics in BFSI

Alright, so far we discussed about use cases and challenges with big data in BFSI. But without the right analytics framework, organizations can not leverage the big data as an asset to their business.

If you are looking to kick-off a new analytics initiative, or upgrade existing capabilities, you should take into consideration the following factors.

A Robust Analytics Roadmap

Start off with a well-defined data strategy. Figure out where you will be collecting data from, what your objectives are, and develop a robust analytics roadmap.

This comprehensive strategy will guide your analytics initiative in the right direction, offering value across departments, and also to business partners and customers. Develop long-term strategies and monitor the analytics, for optimal results.

The Right Analytics Tools and Capabilities

Depending on your data strategy and the analytics roadmap you develop, you need to select analytics tools and platforms that can deliver on your requirements.

  • Data Management Systems
    If we look at the analytics maturity curve, each stage of data management and analytics require different tools. Many times, companies ignore the importance of having a robust data management system which is the foundation of the analytics. The data management can be divided into three major criteria – Database Management System, Data Modeling and ETL. Each step requires specialized tools such as SQL Server, TERADATA, Oracle, Informatica. After this, there is tremendous value that can be collected from this wealth of data. Organizations can then focus on building high-end, dynamic reports by working with tools such as Microstrategy, Qlikview, Tableau and Spotfire. Each tool comes with certain strengths and weaknesses, and depending on the existing infrastructures, feature, budget, data size etc.
  • Pure-play Analytics
    When it comes to pure-play analytics, there are various ways in which banks can gain a competitive advantage. Tools such as SAS, R, SPSS are able to help organizations understand key business questions. Open source tools such as R and WPS enable companies to experiment with different techniques without making huge investments.
  • Machine Learning and AI Capabilities
    Companies, especially those in the financial industry, are going the extra mile to understand their customer base. Today we have the most advanced and sophisticated tools and algorithms that can help analyze not only structured data but also unstructured data. With the advent of machine learning tools such as Python, and technologies such as AI, Natural Language Processing (NLP), Support Vector Machines Learning Algorithm, seamless integration and analysis of disparate data is revolutionizing the BFSI industry.

A Starting Point

There will be a lot of data to process and a lot of issues to address. But start small. Focus on individual problems and how you can remedy them with the help of the insights that you have access too. Don’t get caught up with the sheer amount of data and try to solve multiple problems at once. Make incremental progress, and soon you will not only be able to handle more issues simultaneously, but the you’ll be able to impact the business to a greater extent.

Analytics Outsourcing

Depending on data and analytics maturity of your organization and specific requirement, you can explore working with analytics partner who can take care of everything from technology setup to data management and analytics.

Let’s look at how outsourced analytics with Course5 helps financial enterprises with their analytics and insights requirements.

Case Study: Transforming Big Data with Automated Analytics in BFSI

A prominent national bank wanted a solution to enable faster review and approval of credit applications to quickly onboard borrowing customers, enhance customer experience and effectively manage overall customer life-cycle.

This required a 360-degree view of the business of the bank and its customers, from the big data they were collecting from across channels.

Course5 delivered on the bank’s requirements with an AI-powered one-stop solution. We upgraded their operations with a centralized hub accessing data from various channels and offering complete view of the enterprise, customers, and credit actions.

Through AI-driven customer profiling and module-based scoring, the platform automated all credit actions, enabling faster decision-making with respect to credit application, approval/renewal process and lifecycle management.

Course5 also enabled the platform with an automated monitoring system which provides early alerts and warning when anomalies are detected.

The AI-led accelerated credit management platform streamlined business functions, and was able to help the client experience a 75% reduction in time spent on credit application processes, and a 4x increase in operational efficiency. The early warning capabilities of the platform helped the client better address critical credit approvals/restructuring processes, with increased agility and reduced risks.

A complete view of the business and the customers further enabled faster and more accurate decision-making, improving customer experience and minimizing churn.


Big Data analytics for banking and financial institutions has been benefiting the industry across business functions. There is still immense potential for growth and evolution of the platforms, and the advantages afforded to financial institutions.

However, the BFSI industry is advised to walk with caution. While using various Big Data tools and techniques can reap huge benefits, one should never lose sight of the importance of data security. Companies should not shy away from making significant investments in building a vigorous data governance model and data encryption tools. This might seem insignificant but they are imperative for overall success.  There are already multiple use cases within the industry which stress on the importance of companies using the right tools and techniques to leverage on the power of Big Data in finance.