Daniel Melo, senior director, consulting, at FICO explains how analytics-driven business intelligence tools are helping businesses search through massive databases and cut across organisational silos to identify, prioritise and coordinate actions that fundamentally affect profitability
Banking executives are still under extreme pressure to come up with strategic plans that identify opportunities across departments and reduce risks — be they credit risks, operational risks, reputational risks, conduct risks or other.
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However, they’re struggling with uncertainties arising from tightened regulations, expense constraints, economic turbulence,fragmented organisational objectives and new competitors springing up from every direction. This environment is here to stay and will only become more complicated.
Big Data analytics
So, how exactly do you create an achievable plan, which ensures more money and fewer risks 12 months from now? – with the use of Big Data analytics.
For starters, leading banks are embracing a new approach to strategic planning, enabling them to achieve profit targets sooner through deliberate, coordinated action informed by Big Data analytics. This fully customisable, algorithm approach combines Big Data analytics and domain expertise to examine the main drivers of profit and loss across the product life cycle.
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By GlobalDataPinpointing hidden opportunities and latent risks, it reveals each bank’s most powerful levers for improving results. Through this process, banks have a clearly mapped course to higher profit in just weeks instead of months, which results in a much higher return on investment.
To put this into perspective, one large Asian bank was experiencing significant growth in its credit card portfolio, including a 50% increase in balances over the previous year. Balance-level delinquency rates and losses were low, making the portfolio quiteprofitable. All apparent signs indicated it would maintain such profit margins – all seemed well. However, more granular andcomprehensive analysis indicated signs of trouble within the next 12 months. The bank wasn’t making enough profit on its revenue.
With the use of Big Data analytics, FICO consultants showed that while interest revenue, non-interest revenue and risk-adjusted
revenue were all increasing, yields (percentage revenue to balances) were dropping. This downward trend was likely to worsen, given signs that market competition was increasing and the economy was slowing.
Forecasts
We deconstructed forecasts to identify the main drivers, and found that one reason the bank’s risk-adjusted yield was trending downward was that marketing programmes were generating growth at the expense of revenue.
This is a common problem in growth markets. Promotional pricing drives the numbers of accounts and average balances upward, but cuts into interest revenue. For the bank, this drop was offset partly by rising noninterest revenue, largely from fees on instalment loans offered to cardholders. Nevertheless, this trend would likely be unsustainable as increasing competition from other lenders could force fees down.
Fortunately, these threats to the bank’s future profitability could be countered. A cross-life cycle analysis resulted in a detailed set of recommendations.
Now, the bank has a road map for correcting the future profitability issues in its credit card portfolio and the institution can continue to pursue growth, while avoiding classic pitfalls.
Ongoing cross-life cycle analysis of profit drivers will enable the bank to continue steering a course to sustained profitability. Many factors can affect the plan and its strategies; Big Data analytics create a what-if tool that managers can use to play with different scenarios.
Strategic planners have to get it right by laying out the most effective combinations of revenue drivers, loss reduction measures and cost cuts to deliver higher portfolio profits. Fast-moving financial services markets allow little time for planning and no room for error, so analytics are playing a more crucial role in the process than ever before.
Analytics-driven business intelligence tools are helping business executives search through massive databases and cut across
organisational silos to identify, prioritise and coordinate actions that fundamentally affect profitability. After all, who doesn’t want an ROI of 30:1?
