Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Financial institutions have faced a turbulent economic environment over the past decade.
- It is imperative for financial institutions to be able to gather relevant market intelligence accurately and incorporate it in an agile manner into their forecasts and strategic decisions.
- Streamlining processes, investing in data quality, and leveraging automation to eliminate low-impact activities performed by finance teams will lead to faster and more accurate forecasting.
Overview
Financial institutions have navigated a turbulent economic environment over the past decade. There were major episodes of volatility such as:
- COVID-19 pandemic (2020): The entire globe was impacted by the pandemic, and it triggered a global recession resulting in the most extreme market crash since 2008 and very high volatility in economic indicators.
- Geopolitical tensions: War commenced between Russia and Ukraine in 2022 which led to energy and commodity price shocks.
- High inflation after COVID-19: After the COVID-19 pandemic, due to massive fiscal stimulus and pent-up demand, median global inflation jumped from 1.9% in 2020 to 8.7% by the third quarter of 2022.
- 2025 stock market crash: The sweeping new tariffs levied by US against all countries and steep tariffs against China led to panic-selling in global stock markets, causing the largest decline since 2020.
It’s impossible to accurately predict future economic volatility. Hence, it is imperative for financial institutions to remain agile, gather accurate market intelligence and seamlessly integrate it into their forecasts and strategic decision-making.
Challenges
In the face of volatility, finance functions face several challenges.
- Bottom-up forecasts completed quarterly– Business finance teams don’t have the bandwidth to create forecasts when needed. They end up submitting the trends to central finance teams each month and work with business teams to produce bottom-up forecasts on a quarterly basis.
- Judgement-based forecasts – Forecasts of many crucial parameters such as future loan spreads are judgement based. This makes it difficult to understand the key drivers behind significant variances between forecasts and actuals.
- Manual spreadsheet-based processes – Business finance teams spend an inordinate amount of time creating forecasts for each line of business on spreadsheets and consolidating P&L, and balance sheets for all product lines. They receive multiple inputs from different teams. For e.g. a bank’s business finance team receives growth forecasts such as new loan volumes, new average loan balance etc. from the business, FTP spreads from the treasury team. They then take the prior month’s P&L and balance sheet data, apply forecast assumptions and generate the forecasted P&L and balance sheet. Many financial institutions perform this task manually each month.
- Depending on penultimate month’s data– In many financial institutions, the forecasts are reviewed by the CFO by in the middle of each month. This leaves very little time for finance, treasury, and risk teams to prepare the forecasts. Many of these teams do not have access to last month’s data as they commence working on forecasts. Consequently, they start by utilizing the penultimate month’s data. In a normal business cycle, it may be adequate to use the penultimate month’s data. However, there will be months of significant deviance between forecasts and actuals which require teams to rework their forecasts, leading to delays.
- Lack of bandwidth – Business finance, treasury, risk teams are engaged in multiple BAU activities because of which they lack bandwidth to generate P&L, balance sheet under multiple forecast scenarios and perform stress testing.
- Inaccurate forecasts impact the ability to manage risks in the short-term and strategic planning for the organization in the medium- and long-term. Inaccurate forecasts by business finance teams will impact the liquidity and funding team’s borrowing plan and can increase the borrowing costs.
Solutions
To build agile forecasting process, financial institutions need to look at several solutions to help in streamlining the forecasting process.
- Eliminate spreadsheet/ business-managed applications – FP&A solutions can automate the process of preparing P&L and balance sheet once the forecasts are entered. It will eliminate the need to manually consolidate the P&L, balance sheet from multiple lines of business. This will also give more bandwidth to business finance teams to work on forecasts as compared to manual, low value-add activities.
- Implement driver-based forecasting – Collect market data to build granular driver-based forecasting models. This will help to understand the reason behind the variance between forecasts and actuals. The driver-based models can be built in FP&A solutions so that as the drivers get updated, the forecasted P&L, balance sheet can be prepared seamlessly without manual intervention.
- Business finance teams need to work closely with business teams to understand the trends in market for the financial products and their impact on forecasts. This will help utilize the latest available data for forecasts rather than depend on penultimate month’s data. This will also help to forecasts the more complicated items such as spreads.
- Leverage AI, machine learning for forecasting – Along with data from sales/ business teams, business finance teams can leverage AI/ML for forecasting the new volumes and spreads. This can help them create a better forecast by combining the forecast provided by business teams and that provided by ML forecasting algorithms.
The way forward
Financial institutions need to invest in their systems and redesign their processes to make the entire organization more agile.
We live in an increasingly interconnected global economy, driven by globalization, technological advancements. A crisis in one part of the world can rapidly ripple across the globe. It is impossible to predict a black swan event. To thrive in this environment, financial institutions need to invest in their systems, redesign their processes to make the entire organization more agile. They need to streamline their forecasting processes to make it more agile, invest in systems to automate low-impact activities, thereby creating bandwidth to perform more stress testing. In this way, CFOs will contribute immensely, delivering invaluable insights to business strategy and help navigate their organization through these uncertain times!
