Financial Services

Financial services organisations are no longer simply storing data as required, they’re actively using it in order to generate business insights and add value for their organisation and customers, all thanks to big data analytics.

Most of the big data analytics that these businesses perform happen in real-time to drive immediate decision-making.

Here are five of the most common use cases where banks and financial services firms are finding value in big data analytics:


Fraud Detection

Banks and financial services firms use analytics to differentiate fraudulent transactions from legitimate business dealings. By applying analytics and machine learning, they are able to define normal activity based on a customer’s history and differentiate it from any unusual behaviour indicating fraud. The analysis systems suggest immediate actions, such as blocking irregular transactions, which stops fraud before it occurs and improves the profitability of financial institutions.


Compliance and Regulatory Requirements

Financial services firms operate under very heavy regulations which require a lot of monitoring and reporting This data can be used for trade observations that can recognises abnormal trading patterns.


Customer Segmentation

Banks have been under pressure to change from product-centric to customer-centric businesses. In order to achieve this transformation. One way to achieve that transformation is to better understand their customers through segmentation. Big data enables them to group customers into separate segments, which are defined by datasets that may include:

  • customer demographics,

  • daily transactions,

  • interactions with online and telephone customer service systems,

  • and external data, such as the value of their homes.

Promotions and marketing campaigns can then be more targeted toward customers according the segment that they fall into.


Risk Segmentation

Furthermore, our scorecard solution can be used to help companies mitigate risks by ranking customers relative to a target. For example, in the banking industry, this solution is used to mitigate loan-default risk by assigning accurate credit risk profiles to current and potential customers.



Analysing customers and the market is vital; however, a company’s front-line management and sale staff also has a significant impact on the revenue generated by these firms. Therefore, our workforce analytics solution, Enjol, has been specifically designed to uplift and maintain the performance of these staff members by making use of modern principles of workspace psychology, survey tools and predictive analytic models. Enjol is ideal for companies with a large employee base, where keeping track of the wellbeing of all staff members on a regular basis requires a lot time and resources and is often unrealistic.

A lot of financial firms have already implemented big data projects and are already obtaining a competitive advantage.

Unlocking the Value of Data in the Financial Services Sector