To generate profit and to stay in business are the two most common goals any organization looks after when they think of taking a risk. Higher the risk taken, higher the gain would be. But higher risks may also turn out into higher losses. The banking industry realized it in the market crashes of late 2000s, and since then risk management in banks has changed substantially.
After the global financial crisis, researchers have worked hard to make the credit scoring models accurate. Nevertheless, the availability and accessibility of a vast amount of customer data to banks when combined with the achievements of artificial intelligence, machine learning, numerical mathematics, and statistics; it enabled promising approaches to risk assessment and management. The machine learning algorithms advance themselves by ingesting raw information in large datasets, understanding patterns, and correlations and drawing inferences – something a human could never achieve alone.
The current credit risk workflow is labor intensive and slow, while machine learning prediction models give instant credit decisions. These prediction models automatically use a much broader range of data sources, even including news and business networks. And so they can be used to deliver accurate early warnings and to provide mitigation recommendations, based on historical data. The result is lower rates of default losses and reducing the risk of losing customers to competitors due to a slow process.
Machine learning algorithms can also undertake behavioral analysis by reviewing trade activity of each employee concurrently mining chat-logs and emails to identify suspicious activities. By evolving and searching for new patterns in internal and external, quantitative and qualitative data, the prediction models make real-time decisions to exploit volatility in individual stocks and increase the trading performance of financial institutions while reducing compliance risks.
Be it a liquidity risk, market risk, credit risk, operational risk or any other banking risks; our SIA platform (Softweb Intelligence and Analytics) takes – customer analytics (customer data sets, probability of loan repayment calculation, customer lifecycle value (CLV) calculation and profitability, etc.), transfer prices calculation, risk indicators calculation based on expected and unexpected loss – into account to measure and manage risks.