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A lot of lending had to be done quickly, ignoring risk models and risk appetite, perhaps even undergoing a lighter-touch ‘Know Your Client’ process.
Understanding geographical risks has always been an important part of the credit process as it facilitates; (a) sensitivity to macro economic inputs, (b) compliance with country risk appetite caps and (c) determining legal jurisdiction to feed into LGD inputs and capital calculations.
Linked to this, in the past few years, banks have been trying to understand Brexit impacts – for example, exposure of the lending portfolio to France or Italy.
More recently, COVID-19 impacts have again reminded us of the importance of understanding your borrowers’ supply chains. First we had national lock-downs, but now governments are attempting to prevent second waves via local lockdowns. Risk impacts now need to be understood at a location-specific level.
Detecting fraud in loan books can be difficult using traditional methods; low value write-offs of less than £10k may not be investigated and domain expertise is often siloed into two separate fraud and credit assessment teams.
In the US, we have already seen signs in the PPP scheme of fraudsters taking advantage of government-backed lending stimulus. Huge amounts of debt have been issued in a short period of time, rules have changed multiple times and banks have performed limited assessments of eligibility. Expect remediation exercises to follow.
The EBA lending guidelines specifically call out the need to “bring together prudential standards and consumer protection obligations along with AML and ESG considerations‘’.
Whilst ESG and linkage to credit risk is a developing area, it is rapidly entering the mainstream. In July-20, Moody’s described Lloyds Banking Group’s programme to promote more black employees to senior roles as ‘credit positive’. This marks the first time a rating agency explicitly linked a company’s stability to ethnic diversity measures.
One of the challenges is getting hold of quality ESG data and being able to review metrics, given difficulties with comparing ESG definitions and terminology.
% INCREASE IN FRAUD DETECTION ACCURACY
% Faster Investigations
% ADDITIVE INCREASE IN MODEL ACCURACY
MONTHS BEFORE DEFAULT (CLIENT IDENTIFICATION)
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