AI Based Credit Default Risk

Credit Default Risk

A credit risk is the risk of default on a debt that may arise from a borrower failing to make required payments. The risk of the lender includes lost principal and interest, disruption to cash flows, and increased collection costs. The loss may be complete or partial. Typically, a higher level of credit risk will be associated with higher borrowing costs hence minimizing the business.

Key challenges in the Industry

Are you facing frequent Loss Given Default?
LGD or Loss given default is a very common parameter that always worries financial institutions and lenders. LGD is used to calculate economic capital, regulatory capital or expected loss and it is the net amount lost by a financial institution when a borrower fails to pay EMIs on loans and ultimately becomes a defaulter. It's extremely challenging yet crucial for financial institutions to control LGD.
Are you not able to extend credits in fear of NPA?
The fundamental business model for any bank is to increase their revenues and profitability by collecting interest on the principal amount lent to the various borrowers while minimizing Non Performing Assets (NPA). However, the credit defaulters are the one which slow banks down creating various losses for them. In bank credit analysis, banks consider and evaluate every loan application based on merits so that they can deal with NPAs efficiently. Till date, efficiently monitoring and assessing NPA is one the biggest unsolved problems for 100% of the banks across the world.
Are you facing high Credit default risk?
The risk of loss arising from a debtor is unlikely to pay its loan obligations in full or the debtor is more than 90 days past due on any material credit obligation; default risk may impact all credit-sensitive transactions, including loans, securities and derivatives.
Are you not able to govern & minimize Concentration risk?
The risk associated with any single exposure or group of exposures with the potential to produce large enough losses to threaten a bank's core operations. It may arise in the form of single name concentration or industry concentration.

Benefits with Reliable Credit Risk Analysis

01.
To maximize credit disbursement for higher revenues
A reliable credit analysis tool will help organizations with excellent credit analysis. Checking the credit worthiness of any individual is a mandatory and essential process to understand the extent of credit that can be extended to the individual while keeping the risk close to minimal.
02.
Minimize losses by mitigating Credit Default Risk
An efficient credit analytics tool will monitor and mitigate the Credit default risk which impacts all the sensitive transactions based on credit like loans, derivatives or securities. Credit analytics tools will also assess Credit default risk before approving any credit cards or personal loans.
03.
Minimize banks losses & impact due to Concentration Risk
AI based credit analytics tools are involved in end to end credit management cycles. This means that they are also minimizing the risk which is associated with exposure of any single or group of individuals with the potential to produce large losses to threaten the core operations of a bank.
04.
Increasing profitability with accurate estimations of Credit Losses
The provision for credit losses (PCL) is an estimation of potential losses that a company might experience due to credit risk. The provision for credit losses is treated as an expense on the company's financial statements. They have expected losses from delinquent and bad debt or other credit that is likely to default or become unrecoverable. With AI based credit analytics tools, banks can accurately estimate the credit losses hence achieving the expected business profitability.

Synctactic AI based credit risk mitigation

Synctactic AI Machine learning tools enable organizations to integrate and fine-tune client-related transactional, compliance, economic, demographic, historical, and governance-related data to enhance credit risk management and its analytics. These models efficiently factor PD (Probability of Default), LGD (Loss Given Default), EAD (Exposure At Default) for reliable & scalable lending operations and processes mitigating the credit risks.

AI based Credit Risk Mitigation in Numerous Asset classes

Synctactic AI Machine learning models are applied in various credit risk models and independent credit risk monitoring processes. Banks can use the same modeling steps, control framework, and model validation in numerous asset classes like mortgages, auto loans, student loans, credit cards, unsecured installment, and many more. Synctactic AI based credit risk management models are much more reliable as compared to the traditional models and take care of commercial credit risks and its efficient mitigations while minimizing the NPAs.

AI based Credit risk monitoring & Analysis

Synctactic AI & Machine Learning models add genuine value across the credit risk management value chain, starting from the initial underwriting process to customer portfolio analysis and all the way to risk measurement and analysis to reliably measure the maximum exposure related to credit and credit default risk.

Self Improving Credit Scoring Model

Any faults present in the machinery can be easily pointed out by the sync process of data discovery and analysis. When the fault is detected beforehand, a vast damage can be easily prevented.

Assessing risk for individual customers

Synctactic AI based Machine learning models leverages Data engineering tools sync discover, sync learn , sync data, sync analyze and Neural Network. Such ready to use data engineering tools and networks help financial institutions to create discrete clusters of datasets and apply merging methodologies to figure out if a specific customer should be offered a loan. This means, instead of merely looking at the mean values, ML creates majority and minority clusters and merges them to create a diverse dataset, reflecting the real on-ground picture. This enables organizations to narrow on all non-defaulting individuals in the pool of applicants from the borrower group to whom banks can extend the loan. As a result, a sample of bad customers going into the credit dataset will never cause imbalance and skew results if banks are using AI based analytical models.

To immediately leverage this power of AI on your existing financial and banking systems please connect with one of the data scientist members of the Syntactic.AI team.

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