Why Covid-19 Is Forcing Banks to Rethink Their Credit Platforms

Quick Take

The global pandemic will force banks to make an unprecedented number of credit decisions within a very constricted timeframe. Many banks lack the robust credit platform that can help them respond to such demand. Our combination of financial services expertise and digital and advanced analytics capabilities enables us to work closely with banks to implement powerful, tailored solutions to meet this challenge and thrive in the post-pandemic world. 

The Covid-19 crisis will put severe pressure on banks, and, in particular, on their credit platforms.

As of now, banks are primarily focusing on their key role in managing the emergency financial response, most notably through the activation of state-aid measures and other proactive interventions to support liquidity tensions on their client portfolio.

Over the coming weeks, however, banks will continue to cope with an unprecedented wave of credit decisions. Failure to address those properly will inevitably translate in a huge inflow of non-performing exposures, with heavy impacts on financials and capital adequacy.

The main challenge for banks lies in addressing this situation with credit platforms and models that are largely based on parameters that don’t adequately reflect the stressed market environment and financial situation clients now face. In addition, many banks are likely to be heavily under-staffed--both in terms of the number of employees and the availability of specialized talents and skills--relative to the scale of the challenge.

To respond effectively, banks must step up their game and take full advantage of a range of powerful advanced and digital technologies. Many processes can now be effectively automated as never before, and “smart engines” can enhance decision-making across the entire credit value chain, fully leveraging forward-looking views. 

To achieve this, banks must:
  • Fully deploy forward-looking metrics to evolve their ability to steer the credit portfolio, detecting potential credit issues as early as possible and improving credit decision-making based on the level of attractiveness and resilience of clients. Embracing a data-powered view and leveraging the enormous amounts of information available (both internal and external) can be transformative.
  • Apply innovative techniques, ranging from advanced analytics, optical character recognition, natural language process and machine learning, to improve the performance of their models and to codify and leverage specialized know-how, thus elevating the skillsets and capabilities of a bank’s entire workforce.
  • Radically automate analysis through large-scale deployment of advanced analytics, so that manual tasks such as client assessment can be achieved with a single click, enabling banks to reallocate credit specialists’ precious time to high-value activities.

New advanced technologies now enable banks to combine all the above into an integrated, cutting-edge approach. This is the right course of action.

By structurally rethinking their credit platform and aggressively deploying the right advanced technologies, banks will be able to respond to the crisis more effectively, and unlock significant benefits across the board: 

  • A major reduction in time allocated to credit analysis and decisions thanks to automation, reducing by up to 50% the effort staff now spend on lower-value activities  
  • Strong top-line contribution thanks to a better risk/return profile, a commercial boost on more attractive clients and an enhanced customer experience
  • A significant reduction in the cost of risk, due to a more proactive and effective credit detection and intervention, which translate to lower default rates, higher cure rates and, ultimately, lower loss provisions

How can banks achieve this? Our approach is based on a state-of-the-art suite of solutions that enable banks to enhance their credit management platform across the entire credit lifecycle and value chain, and which can be tailored to address specific use-cases. Our approach encompasses a flexible “sourcing” model that works regardless of the initial level of accessible data. We provide advanced AA/AI solutions that fully leverage all internal and external information, both structured and unstructured. And we deploy advanced algorithms to codify and label all relevant insights and, where needed, replicate expert frameworks. Our suite can be rapidly deployed and each product can be customized and deployed in 12 to 16 weeks.

Among our specialized tools and capabilities:   

Client Analyzer: Our automated client diagnostic tool supports credit analysts with an immediate assessment of client performance and health, with complete automation of client analysis across all key dimensions (economic/financial KPIs, market and industry trends, bank/system facilities overview, and more) on all legal entities files. This reduces by up to 50% the effort staff now spend on client analysis and strategy definition.

Advanced loan origination models: Use forward-looking models to steer origination towards portfolio clusters with the best risk/return profiles, and optimize target corporate portfolio composition, both for short-term and MLT loans, incorporating forward-looking industry and sub-sector level impacts.

Instant/smart lending: We deploy advanced algorithms to automate lending decisions on your most- and least-attractive clients, and help you roll out highly responsive digital lending solutions to both existing and prospective clients, powered by a strong degree of automation.

Forward-looking detection: Advanced early warning systems based on forward-looking variables (internal and external) to effectively identify potentially high-risk clients—several months in advance and with a 20-30 p.p. higher share of risky clients detected compared to traditional models—customized by segments (individuals, SMEs; corporates), and adapted to specific geographical or portfolio characteristics.

Smart-decision engines: Smart-decision engines can suggest the best credit strategy given a client’s specific cluster and behavior and expected performance of potential actions, powered by machine learning. The result is a boost to performance, effective automation of retail collection and an optimal strategy for restructuring files.


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