Recently, data analytics has become very important in the decision-making processes of small and large enterprises. The vast structured and unstructured data generated by many devices in various platforms have given stupendous insights. With the help of data analytics and data management, the Banking and Finance Services Industry (BFSI) has used Big Data to boost organizational success and ensured risk management, profitable growth, and performance.
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The Great Recession of 2008 was the reason why Big Data Analytics became so popular in the financial sector. Consumer confidence in banks declined, investments took a huge hit, demand-driven unemployment was on the peak, and renewed strict regulations were put in place. Banks began to digitize, which meant that they had to deal with cyber security issues like hacking, bots, and computer fraud which could disrupt online financial transactions and services. Banks needed to ensure their clientele that they would protect the clients’ money from all these threats. They had to come up with a solution that could analyze massive data flows which technologies like MapReduce and RDBMS (relational database management systems) fulfilled.
Big Data: The three Vs of Big Data (velocity, volume, and variety) have impacted the banking sector at various points in time. In investment banking, velocity has been a very important factor and so is volume. Whether Cloud Computing or Hadoop, Big Data platforms were introduced to cater to the huge data volume that was being generated.
Banks have been reluctant to employ Big Data technologies, but there is an imminent explosion of Big Data in the banking sector now.
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Here Comes Big Data Analytics!
We are going to list entities in the banking and finance sectors that have optimized their performance due to Big Data Analytics.
1. Marketing: Response modeling is the aim of marketing analytics where effort is put to maximize the response rate considering certain constraints. Customer segmentation is also important in banking which is carried out in the RFM style (recency, frequency, and monetary value). Huge drive for digitization has made customers opt for banks’ Internet banking and mobile banking facilities.
2. Collections: Collections analytics is related to operations analytics which we will discuss in a short while. This type of analytics addresses three questions with regard to bank customers: whom all to contact, how to contact them, and when? It also addresses which customers to contact and whom to ignore. In the case of debt recovery, analytics can help banks decide whether to take the auction route or the distress sale route, along with how to manage foreclosed assets like cars.
3. Risk: To aid banks in reducing risk, mitigating losses, and managing exposure, risk management is very essential. It can minimize credit loss by making overdue payment recovery more efficient. It allows banks to determine credit exposure limits and loan amounts to sanction.
Risk analytics plays an important role in:
- Modeling: Risk modeling involves estimating riskiness through the process of building analytical models. From a risk scorecard, banks assess the risks from customers. Basel norms represent the stress testing, bank capital adequacy, and market liquidity risk of banks which has to conform to global standards.
- Credit policy: Banks develop credit policy and strategy to handle account creation, management, and looking up credit line exposure to different customers.
- Fraud analytics: Analytics is helpful to prevent or detect frauds. Prevention is emphasized more than early detection.
4. Operations: Operations analytics helps banks in optimizing and streamlining operations, like processing name change requests.
This kind of analytics helps banks with queue optimization, process efficiency, people optimization, and incentive optimization.
5. Regulatory norms: Have you watched the movie Too Big to Fail? Without due regulations on financial institutions, the US economy slipped into worst depression and the global economy followed suit. Post 2008 financial crisis, mandatory regulations were imposed on banks in order to avoid such situation. Defying these regulations could result in huge penalties. These regulations refine banks in terms of money laundering, financial crime, terrorism funding, and various other types of fraud. Habib Bank of Pakistani origin was de-licensed recently in the US for terror funding.
6. Human Resources: Like an ant colony, banks are brimming with both white-collar and blue-collar employees in their thousands of branches. How much these employees get paid, how much they should be paid, and, in the first place, how many employees an organization should have, should be well understood by the HR.
Analytics in HR achieves incentive optimization, training effectiveness, attrition modeling, and salary optimization.
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7. Reporting: It is heavily used and provided more sophisticated analyses and modeling (predictive modeling). You may be amazed at how much Tableau and QlikView have revolutionized reporting. Those bank employees who had enrolled for these courses through us are massively using it in their banking functions. Insights can be revealed from dynamic dashboards to the most generic as well as the minutest detail through these tools.
8. Governance: Bank actions need to be in line with government regulations. Stress management models and credit policy must be reviewed, independently.
Banks typically have legacy systems because of which interlinking different types of data from disparate sources is a huge challenge for them.
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How to Operationalize Big Data Analytics?
The steps BFSI has to undertake to operationalize Big Data Analytics are as follows:
- A data platform has to be set up which can retrieve all relevant information. A holistic view of a customer can be provided by strong data management which is provided by competent analytics.
- There have to be strong in-house talent enhancement programs in banks to train the analytics team to be good in technology, data management, and emerging and existing regulations.
- Silos need to be defied, and there should be increased collaboration between analytics and business teams.
- There needs to be a work culture that inculcates decisions based on the insights generated by high-end statistical models.
Roles That Help in Operationalizing Analytics
Data Scientists are among the highest-paid professionals who are key to organizations in understanding the analytics and business aspects of Business Intelligence. They do an important job in the organization by building Machine Learning algorithms and complex models.
1. Chief Data Officer: A person who ensures that the organization is able to optimally extract value from the information
2. Business Analyst: A person who combines the knowledge of analytics with domain expertise. She/he acts as a bridge between analytics resources and business teams.
3. Big Data Specialist: An expert in mining structured and unstructured data, thereby adding insights to a business.
4. Data Literate Workers: Because Big Data has become so important, all employees in the BFSI industry should have a basic data skill set.
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How to Structure the Analytics Team?
We are going to elucidate on the approaches in structuring the analytics team.
1. Decentralized approach: It involves in embedding a small analytics team within each department of an organization. In this model, a group of Business Risk Analysts would report to the Chief Risk Officer and Marketing Analysts would report to the Marketing Head. This way, each group excels in its own domain expertise over a time period.
2. Centralized approach: This approach involves having a center of excellence for analytics. It consists of a group of Data Scientists who do all the important work. Best work practices are easier to share, and all Data Scientists are exposed to new skills quicker than when they would work in smaller teams as in the decentralized approach.
Three Kinds of Analytics Techniques Dominating in the Banking Industry
1. Predictive modeling: It is dominated by regression techniques like logistic, linear, logit, Probit, etc. Machine Learning is also finding its way here.
2. Optimization: As banks are involved in minimizing losses while controlling risk and maximizing revenue, optimization is an important technique. Both linear and non-linear techniques are used widely.
3. Segmentation: It makes use of CART and CHAID algorithms. Random Forest and other Machine Learning techniques are increasingly being used here.
As many banks have legacy systems that are stand-alone, Machine Learning is difficult to implement. It will take some time to optimally use Machine Learning considering its huge potential, but until then other statistical techniques like regression will be in play in the banking sector.
1. Modeling: R, SAS, and Python are the three most popular Data analytics tools in the banking industry for modeling. SAS was prominently used by banks before. Banks were initially hesitant to adopt the open-source R code as they couldn’t claim intellectual property on it. Now, they are using R and Python more and more with R being heavily used in recent years. There have been specialized packages that can do analysis in the banking sector.
2. Optimization: It is widely carried out in Excel, but R and Python host good optimization packages. It shouldn’t be a surprise if optimization shifts to R and Python from Excel in the near future.
3. Segmentation: SAS E Miner is a much-opted tool for segmentation, but it is very pricey. Knowledge Seeker and Knowledge Studio are relatively less priced, and all these tools enable analysts to build decision trees in a GUI-based, user-friendly manner.
4. Visualization and dashboarding: Spotfire, QlikView, Tableau, and SAS visual analytics have revolutionized this domain. CXO (Chief executive officer) dashboards have turned increasingly insightful. In the future, CXOs will give a high-level picture while giving the flexibility to examine the most intricate detail.
Ensuring the Competency of Analytics
Organizations should have knowledge of various processes of analytics. The market of data analytics is a highly dynamic one as new technologies are created in less time than what it takes to develop such skills in employees if the company trains its own employees.
What Are the Challenges in Training Employees for Analytics?
1. Different delivery models: The key to delivering personalized training is to find out what type of content delivery models to use, like e-learning or trainer-based training. Trainees are more benefited when the two approaches are combined. Because of the conflicting schedules of candidates, instructor-based training is difficult. That is why we are providing online training which the candidates can access from anywhere.
2. Varying skill sets: Candidates usually have different skill sets and varying levels of experience and expectations. The training has to be suitable for everyone. Consider some candidates who are familiar with the basics of data visualization and those who are not. Learning Tableau will be easier for those who are familiar with the basics and harder for those who are not.
3. Different modes of training: The training has to be tailored in such a way that it has to cater to all candidates, matching their level of understanding and experience. It should provide assignments and project works based on real-time use cases, as this will help learners understand the analytics topics in-depth.
Performance, profitability, and risk reduction are the main goals that the banking and financial sectors strive to achieve. In this data-driven world, performance is dependent on Big Data technologies that can store and manage semi-structured and unstructured data in real-time. Banks recently are finding it a little hard to comply with all of the local governmental regulations. Sometimes, they have to provide loans at a lower interest rate to certain key sectors, namely, agriculture, housing, and education. Banks should also maintain CRR (cash reserve ratio), SLR (statutory liquidity ratio), repo rate, and various other parameters. BFSI companies have been optimizing operations across all functions. Their aim is to improve efficient systems, enhance service-delivery models and customer engagement, and protect these systems against cyber threats.