AI for operational efficiency
One of the major priorities for leading banks that have invested in AI has been to drive operational efficiencies. Intelligent automation will replace labor-intensive repetitive manual tasks and augment human decision-making. The enterprises of the future will comprise of a hybrid workforce where humans and bots work together. AI has the potential to drive sustainable growth in top-line and bottom-line. The use of advanced analytics and AI to generate insights, automate and manage various backend processes will result in achieving operational excellence and superior customer experience. Some examples of how AI is used to improve business operations are:
Contract intelligence – Supervised and unsupervised machine learning (ML) algorithms can be used to parse and summarize commercial agreements like contract and loan documents, and interpret financial and legal details that would have otherwise required huge manual effort and time. It uses unsupervised ML techniques like topic modeling to identify the key points. It can also summarize a document based on specific keywords using supervised ML techniques.
Cognitive assistants – ML algorithms like NLP and NLG enable cognitive assistants to help customer-facing employees find answers to customer questions. This results in improved TAT and reduced count of employees leading to huge cost savings.
KYC – AI is making the end-to-end KYC and customer onboarding process faster and more efficient. Technologies like NLP and facial recognition enable intelligent bots to process various documents and validate the same removing the need for a manual backend processing team.
AI for risk and compliance management
Banks and other financial service providers, as guardians of financial assets, are liable to put risk control as the foremost priority to foster trust among customers. If responsibly applied, AI can be leveraged to ease up adherence to compliance regulations, reduce costs and free up teams to focus on more valuable tasks within their organizations (See Figure 4).
The key applications of AI in the risk and compliance domain are:
Operational risk analysis
AI enables financial institutions to control fraudulent behavior and delinquency using scoring techniques and real time alerts across different steps of customer lifecycle. These cognitive fraud detection systems focus on customer’s features driving fraudulent behavior to detect and prevent fraud. The ML based system continues to learn, and gets stronger with time to detect more complex fraud. The appropriate usage of ML algorithm could result in the reduction of false positives that improves the efficiency of the acquisition quality, fraud prevention and collection effectiveness.
Credit risk assessment
Data driven credit scoring models help portfolio experts to take conscious decision on credit risk exposure powered by ML algorithms. A credit scoring model, based on information about the potential customer (e.g. credit history, payment defaults, age, number of previous loans, etc.), would be able to distinguish potential defaulters with fair bit of accuracy and estimate of the propensity of default. It also helps to come up with optimized pricing strategy differentiated by risk behavior to control overall portfolio risk exposure.
Analytics can further improve regulatory compliance process of credit risk evaluation using assessment methods like default risk models, loss given default, and exposure at default. These models help to assess the overall risk exposure and take the required measures to mitigate liquidity risk.