Risk Management, Digital Transformation, LGD Estimation

CRIF is among the protagonists of the ABI “Banking Union and Basel 3” event

The value of customer analytics in support of credit risk management in the new market and regulatory context

In a «complex» operating and regulatory context which is undergoing significant developments, the banking system is having to face increasingly significant challenges. The CROs, CLOs, and CFOs of banks will be forced to govern and manage the impacts of neighboring regulations in order to propose strategic and operational policies to the Board and CEO. The challenge will be knowing how to «join the dots» with solid forecasts of interrelations through an integrated, organic and forward-looking approach, explained Giorgio Costantino, Management Consulting & Solutions Executive Director, at the ABI conference.

Today, advanced analytics tools represent an essential way of supporting a bank’s credit risk management and “sustainable” development activities. Some real applications of how customer analytics can support a bank’s activities were presented by Cristina Caprara, Management Consulting Manager. In particular, her talk focused on:

  • Industry forecasts to support credit process management
  • Advanced analytics and machine learning solutions for the reconstruction of chains and income estimates
  • Impairment forecast models and tools within the framework of IFRS9
  • Forward-looking risk estimates to optimize pricing and credit asset management.

Financial & Banking Governance: from regulation to management, a lever for Digital Transformation

Faced with a significant increase in competitiveness, customer relations and engagement must become absolutely central. Banks need to know how to improve reactivity to innovation, reviewing its organizational structures, service model, and governance system. Digital transformation must involve the whole bank with a gradual alignment of all organizational levels to the latest needs and optimization of legacy information systems and infrastructures, commented David Pieragostini, International Markets Executive Director.

In the current digital age, defining a solid information framework to support digital transformation is a priority. Banks have made a lot of effort to introduce risk parameters into credit risk management operating processes. Now they need to establish a data management platform on which to consolidate the digital transformation. Data must guide the bank’s business and operating processes, as well as its approach to the customer, commented Riccardo Ceci, Director for Strategic Alliance.

Through the presentation of case studies, attention was focused on how to build an end-to-end architecture that supports the offer of digital lending products to the customer. In particular, two applications were presented: an instant lending application, which in just 30 seconds enables additional money to be granted to a customer who respects the requirements set out by the bank’s rules, and an application that has a one-hour digital lending journey for credit disbursement, with the retrieval of internal and external data, the request for income documentation, and the application of decision support rules and models.

The inclusion of guarantees in LGD estimation: a possible approach

The correct measurement and monitoring of the quality of real estate collateral must be a priority for the national banking market, which has been disrupted by a significant depreciation in the value of NPEs due to a lack of information or adequate assessment of the quality of assets.

This is certainly the case for the ECB, which added it to the supervisory priorities for 2018 and which, from recent supervisory actions, raised the issue of shortcomings in the completeness and accuracy of property valuations (in particular within the context of secured LGD estimation), explained Gianluca Natalini, RES Consulting Manager.

In response to these needs, CRIF, together with BPER Banca, presented its solution to support the historical reconstruction of collateral values (through the use of the AVM model), facilitating an improved historical interpretation of the phenomena and increase in the performance of secured LGD estimation.

To support the secured LGD estimation, CRIF also presented a new methodological approach which shifts the object of the estimation from the measurement of losses to that of collection, enhancing the use of data characterizing the collateral (size, type, location, reference market, etc.) as variables that can predict the expected recovery.

In this way, the proposed approach is more in line with management practices and lends itself to more versatile and widespread use, for both the performing and non-performing portfolio.

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