Credit risk is the economic loss that emanates from a counterparty’s failure to fulfill its contractual obligations, or from the increased risk of default during the term of the transaction. The increased complexity of assessing credit risk, market risk has opened the door to deep learning in finance. This is evident in the growing credit default swap market where there are many uncertain elements involving determining both the likelihood of an event of credit default and estimating the cost in case a default takes place.
There are various forms of risks that a company faces. These risks originate from competitors, credits, market, etc. The main steps towards managing risks are identifying it, monitoring and prioritizing the risks.
Benefits for the company
Risk Modeling a high priority for the financial industry. It helps them to formulate new strategies for assessing their performance.
Unlike the traditional methods which are usually limited to essential information such as credit score, ML can analyze significant volumes of personal information to reduce their risk.
Type of expertise/ AI domain
Machine Learning and Statistics
Internal data required
Customer Behavioural Data, Historical Transaction Data, Asset Valuation Data
External data possible
Social Media Data/Sentiment Analysis Data/ Market data/ Macroeconomics Data