![]() This will allow them to serve their customers better, grow their business, and compete with fintech companies and attacker banks that are constantly upping their technology games and looking to grab market share.īanks need to implement more automated credit-decisioning models that can tap new data sources, understand customer behaviors more precisely, open up new segments, and react faster to changes in the business environment. ![]() Banks need to implement more automated credit-decisioning models that can tap new data sources, understand customer behaviors more precisely, open up new segments, and react faster to changes in the business environment. In other words, using new credit-decisioning models is not only a powerful way to boost profits but also a business-critical competitive imperative. Banks need to identify such companies quickly. Making matters more urgent is the looming wave of business defaults expected as governments begin to ratchet back their unprecedented support. For example, such overlays might assign a high likelihood of default to the hospitality sector in London without differentiating between a restaurant that has rapidly transitioned to an omnichannel model (that deals with business interruptions and lockdowns) and one that has not. Some banks have applied model overlays that are subjectively derived and are not precise enough for underwriting-often at an industry or geographic level. Particularly troubling is that many credit-decisioning models today rely on historical data that are virtually useless, given the market disruptions caused by the COVID-19 pandemic. However, the strategy is considerably less tenable as customer information becomes more democratized via “open banking” and regulations such as PSD2-and as fintech companies and attacker banks proliferate and focus on an increasingly digitally savvy customer base. That was viable when banks could rely on their incumbent positions to preserve market share and profitability. In the past, banks updated the models only every five to ten years. In addition to these benefits, there are serious downsides when banks do not put next-generation credit models in place. Use of the new models have resulted in 20 to 40 percent improved efficiency, thanks to a combination of more highly automated data extraction, case prioritization (for example, using straight-through processing for low-risk cases while analyzing higher-risk cases more thoroughly), and model development.Ī business-critical competitive imperativeīased on those three benefits of improved credit-decisioning models, the average bank with €50 billion in assets from small and medium-size enterprises (SMEs) could see €100 million to €200 million of additional profit. That element affects the levels of provisions and capital that a bank must hold. Companies have seen a decrease of 20 to 40 percent in their credit losses by using models that could more precisely determine customers’ likelihood to default. This also results in faster executions that reduce the typical price slippage observed with longer timelines. Meanwhile, a credit-decisioning model that automates large parts of the assessment process and eliminates paper-heavy steps lowers the cost of acquisition and improves the customer experience. By better distinguishing between creditworthy and noncreditworthy customers, banks can improve acceptance rates and pricing. The new models have led to a revenue increase of 5 to 15 percent through higher acceptance rates, lower cost of acquisition, and better customer experience. Banks that have already embedded high-performance credit-decisioning models into their digital lending have reaped three key benefits: ![]() But the benefits of overcoming them should not be downplayed either. These challenges are real and should not be downplayed. They face significant capability, technology, and cultural hurdles, including a limited set of data sources simple analytical engines a heavy reliance on subjective assessments from relationship managers (RMs) and underwriters outdated, inflexible models that have been patched over time and concerns about the length of implementation and regulatory reviews. Many banks struggle with transitioning to a more advanced credit model. In this article, we share four best practices that we have observed when designing new or upgrading existing credit-decisioning models. They have performed well, while traditional models have struggled to handle the changing customer circumstances, forcing banks to resort to Band-Aid solutions (for example, expert adjustments of default rates at portfolio-segment levels). Use of the new credit-decisioning models during the COVID-19 pandemic showcased their benefits. ![]()
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