While most banks are digitizing parts of their business and operations, many are dissatisfied with progress when it comes to credit automation. A few familiar frustrations include legacy IT systems; a general lack of trust in automated decision making; insufficient cooperation between diverse bank functions; limited access to data; and scarce digital talent. Moreover, a number of stakeholders need to align and remain constantly motivated to work together over a prolonged period (two to three years in banks that have executed ambitious programs successfully) for the organization to fully transition to an automated process.

Because of these barriers, programs launched with great executive attention and focus lose momentum as the initial excitement over the initiative evaporate. Here are five practical lessons that have emerged from numerous banks that have successfully digitized the credit journey, with special emphasis on SME lending, the area that is currently getting the most attention and investment.

1. An end-to-end journey but with limited scope

Many banks have found that an end-to-end view of the entire customer journey, including a target state set according to the customer experience, was crucial to success. For example, a European bank redesigned its business-lending process from end to end, allowing it to eliminate numerous handovers resulting in 30% greater efficiency. Attempts to improve the credit process piece by piece tend to become incremental, lose customer focus, and miss the big-picture opportunity to deliver a fundamental step change in performance and approach.

Once you have defined an end-to-end view,successful banks have learned that it pays to limit the scope of the first wave of the transformation and focus on a minimum viable product (MVP). The MVP is scoped to be substantial enough to drive real value, momentous enough to create excitement within the organization, and simple enough to be designed and implemented rapidly.

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2. Consider fintech partnerships

At some traditional banks, the underlying credit processes cannot be made to support real-time and online lending journeys. In McKinsey’s Future of Risk Management Survey, 85% of risk managers viewed legacy IT infrastructure as the main challenge in digitization. To overcome this challenge, many large financial institutions have partnered with fintechs. The partnerships enable banks to develop new capabilities and present new customer offerings more quickly.

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3. Building momentum for full automation

Traditional banks can be wary of a fully automated approval process for business loans. Long-standing policies and decision processes often depend on manual reviews and cross-checks. Successful banks overcame this hindrance in two ways when introducing automation. First, to establish accuracy, many banks test models on past decisions. Second, banks start small, at first directing only a few cases to the fully automated straight-through digital process flow. A bank in Scandinavia ran its newly developed decision engine on all applications from the past years. The tests proved that the automated engine based on data-driven assessments was better at predicting default risk than the subjective human assessments had been—and far more consistent.

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4. Embrace relationship managers

Relationship managers play an important role in SME lending. Digitization doesn’t replace this. While for some segments it makes sense to steer customers into a mostly self-service approach, successful banks have typically opted for a “multichannel, single application” route for SME lending, where customers can complete digital applications on a shared screen with their RMs. This allows the RM to guide the customer through the process, explain results of automated risk assessments, and quickly ask any follow-up questions required. In such cases, banks were able to provide loan approval in five to ten minutes about three-quarters of the time; more complex cases are decided in an average of 90 minutes (and not more than 24 hours) following a manual review.

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5. Big data—but not too big

While creative use has been made of unusual data sets, including novel sources such as social media, it is usually best to begin with readily available data. Transactional data have proved especially powerful. A number of banks and fintechs have developed tools to process transactions from primary operating accounts line by line, classifying them into detailed revenue and expense items. Advanced analytics can use these rich risk data to generate simplified financial statements, affordability ratios, customer- and supplier-concentration analyses, and so on, in real time. These transactional data offer substantially richer and more up-to-date insights about company performance than out-of-date annual accounts.

Ready to use fintech to improve your bank's risk assessment of commercial loans? Schedule a call with us here and we will walk you through what we can do to jumpstart growth.

While the challenges in digital-lending transformations are formidable and the path to ultimate success can be bumpy, experience proves that the efforts expended are more than fully repaid in competitiveness and profitability. Success means much faster credit decisions, with customers getting cash up to 80% sooner; lower costs, with 30 to 50 percent less time spent on decision making; and better-quality risk decisions, which translate into greater profitability down the road.

Source: McKinsey.com

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