Why Banking and Financial Services Industry Is Betting Big on Data Analytics?

Lately, data analytics has become a game-changer for decision-making in both small and large businesses. The enormous amounts of structured and unstructured data produced by various devices across different platforms have provided incredible insights.

Thanks to data analytics and effective data management, the Banking and Finance Services Industry (BFSI) has harnessed Big Data to enhance organizational success while ensuring risk management, profitable growth, and improved performance.

The Great Recession of 2008 was a turning point that propelled Big Data Analytics into the spotlight within the financial sector. During this time, consumer trust in banks plummeted, investments took a nosedive, unemployment surged due to demand-driven factors, and stricter regulations were enforced.

In response, banks began to digitize their operations, which brought about new challenges like cybersecurity threats, including hacking, bots, and computer fraud that could jeopardize online financial transactions and services.To reassure their clients, banks needed to demonstrate that they could safeguard their money against these risks.

They sought solutions capable of analyzing vast data streams, which technologies like MapReduce and RDBMS (relational database management systems) were able to provide. When it comes to Big Data, the three Vs—velocity, volume, and variety—have influenced the banking sector at different times.

In investment banking, both velocity and volume have been crucial. Whether through Cloud Computing or Hadoop, Big Data platforms were developed to handle the massive data volumes being generated. While banks have been hesitant to adopt Big Data technologies, there’s now a significant surge of Big Data in the banking sector.

Enter Big Data Analytics!

Let’s see how some entities in the banking and finance sectors that have optimized their performance thanks to Big Data Analytics.

1. Marketing: The goal of marketing analytics is response modeling, where efforts are focused on maximizing the response rate while considering specific constraints.Customer segmentation is a key aspect in banking, often done using the RFM method, which stands for recency, frequency, and monetary value. The push towards digitization has led many customers to prefer online and mobile banking options offered by their banks.
2. Collections: Collections analytics ties into operations analytics, which we’ll touch on shortly. This type of analysis focuses on three main questions regarding bank customers: who to reach out to, how to get in touch, and when to do it? It also helps identify which customers should be contacted and which ones can be overlooked. In terms of debt recovery, analytics can guide banks in deciding whether to pursue auctioning off assets or opting for a distress sale, as well as how to handle foreclosed items like vehicles.
3. Risk: Effective risk management is crucial for banks to lower risks, minimize losses, and manage their exposure. It can enhance the efficiency of recovering overdue payments, thereby reducing credit losses. Additionally, it helps banks set limits on credit exposure and determine appropriate loan amounts to approve.

Big data analytics in BFSI Risk analytics is vital for

• Modeling: Risk modeling is about estimating potential risks by creating analytical models. Banks use risk scorecards to evaluate customer risks. Basel norms guide stress testing, capital adequacy, and market liquidity risk, ensuring compliance with global standards.
• Credit policy: Banks formulate credit policies and strategies to manage account creation, oversee account management, and monitor credit line exposure for various customers.
• Fraud analytics: Analytics plays a significant role in preventing or detecting fraud, with a stronger emphasis on prevention rather than just early detection.

4. Operations: Operations analytics aids banks in optimizing and streamlining their processes, such as handling name change requests. This type of analytics supports banks in improving queue management, enhancing process efficiency, optimizing workforce allocation, and refining incentive structures.
5. Regulatory norms: Have you seen the movie “Too Big to Fail”? It really highlights how, without proper regulations on financial institutions, the US economy fell into a deep depression, dragging the global economy down with it. After the 2008 financial crisis, strict regulations were put in place for banks to prevent a repeat of that disaster. Ignoring these rules can lead to hefty penalties. These regulations help banks tackle issues like money laundering, financial crimes, terrorism funding, and various other types of fraud. For instance, Habib Bank, which has roots in Pakistan, was recently stripped of its license in the US due to involvement in terror funding.
6. Human Resources: Think of banks like a bustling ant colony, filled with thousands of both white-collar and blue-collar workers across their many branches. It’s crucial for HR to have a clear understanding of employee salaries, what they should be earning, and how many staff members the organization actually needs. HR analytics plays a key role in optimizing incentives, measuring training effectiveness, modeling attrition, and fine-tuning salaries.
7. Reporting:This area is heavily utilized and has led to more advanced analyses and modeling, especially predictive modeling. You might be surprised at how much tools like Tableau and QlikView have changed the game in reporting. Bank employees who took our courses are now leveraging these tools extensively in their daily operations. They can uncover insights from dynamic dashboards, ranging from broad overviews to the tiniest details.
8. Governance: Banks must ensure their actions align with government regulations. It’s essential to independently review stress management models and credit policies. Many banks still rely on legacy systems, which makes it quite challenging to connect various types of data from different sources.

How can we make Big Data Analytics a reality in the BFSI sector?

First off, we need to establish a robust data platform that can pull together all the relevant information. With effective data management, we can gain a comprehensive view of our customers, thanks to skilled analytics.

Next, banks should invest in strong in-house talent development programs to equip their analytics teams with the necessary skills in technology, data management, and both emerging and existing regulations. It’s crucial to break down silos and foster greater collaboration between analytics and business teams. We also need to cultivate a work culture that encourages decision-making based on insights derived from advanced statistical models.

Now, let’s talk about the roles that are vital for operationalizing analytics: Data Scientists are among the top earners in the field, playing a crucial role in helping organizations grasp the analytics and business sides of Business Intelligence. They’re responsible for creating Machine Learning algorithms and intricate models.

1. Chief Data Officer: This individual ensures that the organization maximizes the value extracted from its data.
2. Business Analyst: This role merges analytics knowledge with domain expertise, acting as a vital link between analytics resources and business teams.
3. Big Data Specialist: An expert in extracting insights from both structured and unstructured data, enhancing business intelligence.
4. Data Literate Workers: Given the significance of Big Data, it’s essential for all employees in the BFSI industry to possess a foundational understanding of data skills.

How to Structure the Analytics Team? Let’s dive into some effective ways to structure your analytics team.

• Decentralized approach: This method involves placing a small analytics team within each department of the organization. For instance, a group of Business Risk Analysts would report directly to the Chief Risk Officer, while Marketing Analysts would connect with the Marketing Head. This setup allows each group to really hone in on their specific area of expertise over time.
• Centralized approach: Here, we create a center of excellence for analytics. This team, made up of Data Scientists, handles all the critical tasks. Sharing best practices becomes much easier, and Data Scientists can pick up new skills more quickly than they would in smaller, decentralized teams. Three Key Analytics Techniques Dominating the Banking Industry

1. Predictive modeling: This area is largely driven by regression techniques such as logistic, linear, logit, and Probit. Machine Learning is also starting to make its mark here.
2. Optimization: Since banks aim to minimize losses while managing risk and maximizing revenue, optimization techniques are crucial. Both linear and non-linear methods are widely utilized.
3. Segmentation: This technique employs algorithms like CART and CHAID, with Random Forest and other Machine Learning methods gaining traction.

Many banks still rely on legacy systems that operate in isolation, making it challenging to implement Machine Learning. While it may take some time to fully harness the potential of Machine Learning, traditional statistical techniques like regression will continue to play a significant role in the banking sector for now.

Big Data Tools Required in the Banking Sector

1. Modeling: When it comes to modeling in the banking industry, R, SAS, and Python are the top three data analytics tools. SAS used to be the go-to choice for banks, but there was some hesitation to embrace the open-source R code since they couldn’t claim ownership over it. However, times have changed, and banks are increasingly turning to R and Python, with R seeing a significant uptick in usage lately. There are now specialized packages tailored for analysis in the banking sector.
2. Optimization: While Excel has been the traditional tool for optimization, R and Python are stepping up with some impressive optimization packages. It wouldn’t be surprising to see a shift towards R and Python for optimization tasks in the near future.
3. Segmentation: SAS E Miner is a popular choice for segmentation, but it comes with a hefty price tag. On the other hand, Knowledge Seeker and Knowledge Studio are more budget-friendly options, allowing analysts to create decision trees in a user-friendly, GUI-based environment.
4. Visualization and Dashboarding: Tools like Spotfire, QlikView, Tableau, and SAS Visual Analytics have truly transformed this area. CXO (Chief Executive Officer) dashboards are becoming more insightful than ever. Looking ahead, CXOs will be able to provide a high-level overview while still having the flexibility to dive into the most intricate details.

What Are the Challenges in Training Employees for Analytics?

1. Different Delivery Models: The secret to effective personalized training lies in choosing the right content delivery models, whether that’s e-learning or instructor-led sessions. Trainees benefit the most when these two approaches are blended. However, instructor-led training can be tricky due to the conflicting schedules of candidates. That’s why we’re offering online training that candidates can access from anywhere.
2. Varying Skill Sets: Candidates come with a mix of skill sets, experience levels, and expectations. Training needs to cater to everyone. For instance, some candidates might already be familiar with the basics of data visualization, while others may not have any background at all.Learning Tableau can be a breeze for those who already have a grasp of the basics, but it might be a bit of a challenge for those who don’t.
3. Different modes of training: The training needs to be customized to meet the diverse needs of all participants, aligning with their understanding and experience levels. It should include assignments and projects based on real-world scenarios, as this approach will help learners dive deeper into analytics topics.

Conclusion

In the banking and financial sectors, the primary goals are performance, profitability, and reducing risk. In our data-driven age, achieving performance hinges on Big Data technologies that can handle both semi-structured and unstructured data in real-time. Lately, banks have been facing some challenges in keeping up with local government regulations.

A Business Analyst course covers essential skills like data modeling, requirement gathering, and optimizing business processes. At times, banks may need to offer loans at lower interest rates to key sectors such as agriculture, housing, and education. They also have to keep an eye on the cash reserve ratio (CRR), statutory liquidity ratio (SLR), repo rate, and other important metrics.

BFSI companies are working hard to streamline operations across all areas. Their goal is to create more efficient systems, improve service delivery and customer engagement, and safeguard these systems from cyber threats. Enhance your data-driven decision-making skills with our Data Analytics course in Bangalore, designed to equip you with the in-demand skills needed in today’s job market.

Ready to step into the amazing word of analytics? Contact us today!