Credit Finance

Credit Finance

Advanced Machine learning and Deep Learning algorithms can help creating BASEL compliant IRB/AIRB models to formulate PD (Probability of Default), LGD (Loss Given Default) or EAD (Exposure At Default) models, because they can find relationships that traditional methods cannot.
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Credit Finance

decrease in NPA

decrease in expected loss

increase in capital ratio

AI in Credit Finance

Our deep credit models take into account both application level/ loan performance level data augmented by other information as economic and demographic data provide a much higher accuracy than legacy models. Our explainable AI models decipher the data interdependence in the model and provide actionable insights

The credit risk model landscape is heavily regulated. Using our explainable AI and advanced ML models which look into transaction, performance , social media, etc. we provide high accuracy, preferred degree of conservatism and keeps models in compliance with regulatory requirements

Probability of Default

Given a default scenario, our AI models can help to model or predict the Loss (LGD) which traditional models can't find due to the bimodal nature of distribution. We take into view long and short term customer and economic conditions to model the loss under BASEL guidelines helping to optimise capital

Loss Given Default

The last block in the expected loss models is the exposure that banks have at default. Using both IRB and AIRB models, we can predict the exposure and aid in the institution's capital management. This helps in further growth of the banks' business yet maintaining requisite capital in case of downturn

Exposure at Default

Our VaR forecast use Artificial Neural Networks (ANNs) instead of HS or GARCH. Based on Mean Loss Comparison, Violation Ratio and Christofferson’s conditional coverage test, both the simulation and real data results prove that the ANN combinations have superior forecast performance than the individual VaR models

VaR models

Based on a derivation of Smets Wouters models, this is an elaborate New Keynesian model where Wages and Prices are Calvo-sticky and numerous sources of shocks and interventions are used to correctly model or simulate the economy. This helps in stress testing the banks' capital in different economic conditions.

CCAR models

With the changing landscape of the amount of data available, new drivers like social media, sustainability information heavily impacts credit risk model especially in micro finance and wholesale models. While these create very accurate systems, we should also heed to making the models GDPR compliant.

New Age Models

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