In today’s data-driven world, financial institutions need sharp analytics to make informed decisions. In an Interview with BW CFO World, Satish Gundewar, CFO, CSB Bank, shares Insights on how advanced financial analytics are empowering smarter banking strategies
In your experience, how can advanced financial analytics be most effectively leveraged to empower strategic decision-making within an organisation?
Financial analytics is the process of collecting, analysing and interpreting data from financial sources to gain insights and make better business decisions.
Organisations use data analytics to analyse vast amounts of data to identify trends, patterns and anomalies that can help them make informed decisions. It can be used for various purposes such as Financial Management, risk management, fraud detection, customer segmentation and marketing. With the help of financial data analytics, financial institutions can make better decisions, reduce risks and improve their overall performance. These days in any organisation, data is the most valuable asset. Organisations are differentiated based on how they are able to capture the data, organise the data, analyse the data, how they are able to draw conclusions based on the data and then what actions are taken based on the inferences which are drawn post analysis of data.
Banking and Financial services companies are largely data driven organisations. Day in and day out they deal with millions of data points relating to customer financial transactions. They also have a fiduciary responsibility to maintain customer data confidentiality. The data which is captured can be broadly categorised into customer specific data and Financial data. Let’s first talk about customer data. In the current digital age and with surging digital transactions especially UPI transactions/ Credit card transactions, companies can identify specific geographies where they can open branches catering to specific needs of the customers. They can target asset branches in places where they see good potential for advances of various types like mortgages, vehicle loans, agri loans, gold loans and so on. At the same time, they can identify locations where they need to have a presence to garner deposits and wealth management products. These companies can make use of the data to acquire new customers and offer them customised solutions based on their needs. Companies can also run customer acquisition campaigns as well as run campaigns for existing customers to increase the wallet share from these customers. These days Banks run campaigns only for a specific set of customers. These could be aimed at either activating these customers, or to deepen their relationships by cross selling various products or aimed at opening accounts of their family members. Data can also be used to make these customers digitally active so that physical branch centric transactions can be reduced. All this is possible if organisations make best use of Data Analytics.
How would you approach fostering a data-driven culture within the finance team and how would you encourage collaboration with other departments to utilise financial analytics for better decision-making across the company?
Finance functions wear multiple hats and are not just limited to publishing Financial results. Finance teams perform the Business partnering roles and act as enablers for the business teams to achieve their financial targets by providing them with cutting edge analysis. They are not only able to show the past performance, but also able to provide near accurate estimates of what are the likely forecasts. Any business vertical or a product has multiple key performance indicators. For eg – any advances (loans ) business would need to see what is the portfolio yield, applicable cost of funds and resultant spread. What was the disbursement and what was the yield for the new disbursement, what was the level of attrition and the yield on the attrited book, what repricing’s have happened in the month, what is the relationship between the change in cost of funds and the portfolio yield, whether business is able to maintain its margins both in increasing interest rate scenario as well as falling interest rate scenario, what fees it is able to generate as a percentage of average assets, direct and indirect opex relating to business, credit costs for the products, Slippages as well as upgrades and its impact on the net interest margin, cross sell income so on and so forth. Which customers are profitable for the bank by looking at the 360-degree customer view. How to upgrade or downgrade the customers based on their relationship value for the company. What is the customer lifecycle and what kind of trends it is projecting. For deposit/liability customers, whether we are able to increase their relationship value over the years or only new customers are contributing to the growth in the portfolio. All the above are just an example of what finance teams can contribute to the Business.
Today with multiple tools available, finance teams are able to leverage on the data and come with valuable insights. They are able to guide the business teams to change product mix, make changes to the product construct, rationalise the incentive structure for their frontline staff, run customer centric campaigns and also measure the efficacy of these campaigns and analyse the cost vs benefits of running these campaigns. Finance team guides the businesses to make course corrections so that they are able to achieve their financial/ non-financial Goals.
What are your views on the role of technology and financial analytics tools in optimising financial processes and generating valuable insights?
Technology and Financial Analytics tools play an extremely important role in empowering the businesses, Management teams as well as the finance team to drive business decisions. These tools help management immensely in formulating the business strategy as well as in its execution. However, it is far more important to ensure that these technology tools are well implemented through a futuristic thought process and the right data is captured at the right granularity so that multiple customised analytics can be run on these data sets. These Analytic tools are data hungry hence technology teams play an important role to ensure that the right kind of data is made available, data is aggregated and organised at one place.
Create a single source of truth by doing proper inter system reconciliations. All this is possible by a well thought through system integrations. These are some of the very complex tasks and they determine how the organisation will function in future.
How would you integrate advanced financial analytics with existing financial processes and reporting structures to ensure seamless information flow and efficient decision-making?
As organisations evolve and grow bigger there is increasing need and demand on data and advanced financial analytics. With bigger balance sheets, smaller changes in a few basis points in any important financial parameter make bigger impacts on the outcomes and bottom-line. At this phase of the organisation, the ability of the teams to dig deeper into data at faster speeds, identify the root causes and take corrective actions is vital.
Today is an era of data explosion. Many of the existing financial processes are designed to provide regular updates to businesses and functions and they generally run into multiple pages. This makes the task of a decision maker difficult. Many important messages get lost in these huge piles of numbers and ratios. It is very important these days for the finance teams to be very objective. Most of the finance team members feel that showing variances between different periods or between the plan and actuals, putting in a few ratios and key performance indicators will suffice.
However real financial analysis is to go behind these numbers and identify the “Why” behind all these numbers. Showing “What” is always easier. Finance teams need to be trained to have this kind of mind-set to go behind “why” rather than “What”. Every month it is essential to identify only such a handful of indicators, go behind the “why” and have constructive discussions with the business heads. Many times these are some of the early indicators, which need immediate course correction before their impact actually blows out. The ultimate benefit of investment into technology, Various analytical tools and data analytics is the ability of the organisation to arrive at conclusions based on cutting edge financial analytics and take appropriate and timely decisions.
In your opinion, what are the emerging trends in financial analytics and how can companies leverage these trends to gain a competitive advantage?
According to me the following are the emerging trends in Financial Analytics space are:
Data Analytics
Reporting is faster and more easily accessible with recent digital tools. Technological advances are enabling companies to gain real-time access to data, thanks to advanced analytics. It’s nothing new for financial businesses to use data analytics to help get valuable insights, identify process improvements or help companies manage risk better. But now, companies can use artificial intelligence (AI) and machine learning (ML) capabilities to enable enhanced data to support more advanced analytics and data visualisation tools to deliver insights in real time.
Predictive Analytics
Predictive analytics is the use of statistical algorithms and machine learning techniques to predict future outcomes based on historical data. With the help of predictive analytics, organisations can forecast trends and patterns, identify risks and make informed decisions. Predictive analytics can be used for various purposes such as risk management, fraud detection, possible delinquencies and defaults and customer segmentation. As predictive analytics continues to evolve, its applications in financial data analytics will become even more significant.
Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning are transforming the financial industry by enabling organisations to analyse vast amounts of data quickly and accurately. With the help of AI and machine learning, organisations can automate their processes, reduce costs and improve their decision-making. AI and machine learning can be used for various purposes such as fraud detection, risk management and customer service. As AI and machine learning continue to evolve, their applications in financial data analytics will become even more significant.
Big Data and Cloud Computing
Big data refers to large and complex data sets that cannot be analysed using traditional data processing methods. With the help of big data analytics, organisations can gain valuable insights into their operations, customers and markets. Cloud computing is also transforming the financial industry by providing organisations with the computing power they need to process large amounts of data quickly and efficiently.