AI, machine learning, and the future of credit risk management

For banking majors, credit risk has always been a challenging area, given the multiple factors that go into forming an individual's risk profile. For business borrowers, the process is even more complicated as data across a variety of parameters and time periods must be aggregated and analyzed to create a holistic picture of risk. And the stakes are extremely high for lending banks -- inaccurate assessments can cost organizations sizeable amounts. This is further intensified by sub-optimal underwriting, inaccurate portfolio monitoring methodologies, and inefficient collection models. Clearly, it is imperative for banks to adopt smarter models of credit assessment that can parse huge volumes of data in truncated timelines, dynamically altering risk profiles as per real-time data.