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?
Are you not able to extend credits in fear of NPA?
Are you facing high Credit default risk?
Are you not able to govern & minimize Concentration risk?
Benefits with Reliable Credit Risk Analysis
To maximize credit disbursement for higher revenues
Minimize losses by mitigating Credit Default Risk
Minimize banks losses & impact due to Concentration Risk
Increasing profitability with accurate estimations of Credit Losses
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.