5 Advanced Analytics Use Cases in Banking and Financial Services Industry

Banking and Finance institutions have no choice but to comply with changing compliance rules, and they are constantly bearing the brunt of ever-growing scrutiny by the public, law agencies, and statutory and regulatory bodies. What makes it more difficult is when some banks still rely on inefficient traditional technologies and legacy systems, which effectively leads to loss of effort, money, brand quality and more importantly lack of necessary insights to function properly.

With the advent of technology and data revolution over the last decade, advanced analytics has gained significant ground in the banking and finance sector, offering enormous potential for these organizations to grow and stay relevant. Many banks have already started building their analytics capabilities, tapping into the deluge of data to transform and gain actionable insights for enabling faster and more accurate decision making.

Going beyond the traditional business intelligence (BI) advanced analytics involves components, technologies and methods such as pattern matching, data mining, sentiment-analysis, predictive and prescriptive analytics, machine learning, natural language processing, forecasting and others.

The first phase in the analytics journey begins by employing descriptive analytics techniques, looking at the past performance of the organization. It uncovers hidden patterns that provides insights in terms of customer behaviour, sales trends, product performance, risk patterns and others. By democratizing this information, banking institutions can help their employees gain powerful information at their finger-tips, enabling them to perform better.

The next phase in the journey is incorporating advanced analytics techniques such as predictive modelling, machine learning, forensic analytics and others. Advanced analytics can equip institutions with more powerful tools and technologies to predict sales trends, create ‘what-if’ scenarios, identify and act on customer sentiment, segment customers for customized product promotions and identify and act on risks in advance before they even occur.

Advanced Analytics combining these components and technologies empower and enable business users to search, conduct, and analyse forecasts and predictions. Banking and Finance organizations can gain timely and precise insights for arriving at business decisions.

Let’s have a brief look at five real-world 10xDS Advanced Analytics use cases in the Banking and Financial Services Industry:

1. Operational Risk Dashboard

An Operational risk dashboard offers a web-based view of the risk exposures to the client. The solution leverages descriptive analytics, providing latest insights into risk data and features tools to slice and dice, drill down, filtering and more, for the risk leadership to make informed decisions. Banks can consolidate and refresh the risk dashboard periodically.

2. Forensic analytics

Employing Advanced analytics techniques,  Banks and Finance organizations can learn, understand and analyse fraud transactions that occurred in the past along with its trends, patterns and other parameters.  Advanced modelling techniques could be used to build a machine learning based predictive model that predicts the probability of any fraudulent transactions, thus minimising the risk of fraudulent transaction occurrence.

3. Predictive Maintenance

Advanced analytics, machine learning techniques and predictive models can be used to detect the probability of ATM failures, enabling better utilization of maintenance staff and significantly reducing operational expenses.

4. Application screening

Predictive modelling and machine learning techniques can be utilized to create a model which accepts the customer details and predicts the probability of the customer defaulting. Bigdata technologies can help in building an efficient screening process. The solution can also assess repayment capability of a customer by looking at various parameters which is usually impossible via manual screening. It also reduces the probability of an asset turning into a Non-Performing Assets (NPA).

5. Customer Analytics

Advanced analytics techniques can be leveraged to combine big data sets such as customer demographics, key characteristics, products held, credit card statements, among others to classify the customer base and identify similarities and create micro segments among the customer base. This can help banking and finance organizations to customize marketing campaigns for each individual micro segments by defining “next-best-product-to-buy” models, improving the effectiveness of such campaigns.

Conclusion

Exponential Technologies such as Advanced Analytics are transforming the banking world, improving profitability, compliance, competitiveness and helping shape business decisions at a faster rate. You may find developing bank’s analytics capabilities in your organization daunting at first, but you can start small by beginning with some doable steps, integrating data analytics into operating models and then expand from there to get ahead of the curve.

How 10xDS can Help?

10xDS is driving digital transformation by leveraging our expertise on data and analytics technologies and deep sector experience to enable organizations make effective strategic decisions.  Our strong portfolio of data and analytics services includes Information Management, Big Data, Business Intelligence and Advanced Analytics solutions for assisted and semi-automated decision making.

To learn more about how to kickstart your advanced analytics initiative in your Banking or Finance organization, talk to our Analytics Experts!