Decrease risk by predicting delinquency, increase profitability by combating insurance fraud, and improve your customer satisfaction by detecting and thwarting consumer fraud before it happens.
From banking to insurance, there are countless sets of detailed consumer and transactional data available within the financial services industries. Numtra helps businesses mitigate risk and drive value so institutions can better serve their customers and maintain profits.
Machine learning helps banks mitigate risk by better determining credit limits for potential customers. Past transactional and customer data are fed into a model that predicts an appropriate credit limit. As more data is collected from the customer and available to the algorithm, the credit limit can be adjusted appropriately. This helps financial institutions identify customers that are likely to default and manage risks more efficiently. Machine learning is also implemented by insurance companies to predict customer fraud before claims are paid out and money is lost.
Non-banking Financial institutions also do a lot of back end work like automation of bank statements processing using NLP techniques, monitoring credit bureaus for any loans after applying for mortgage loans and other cognitive process automation that can be done using data science and machine learning technologies. Banks and insurance companies can analyze available data to improve their business and attract more customers. NLP and data mining identify market trends, so businesses can adjust their product offerings to be more appealing to current consumers. Data is constantly being collected from social media and mobile applications, and machine learning algorithms gain insights from client behavior and feedback to increase the business’s value to consumers.
Numtra uses advances in machine learning and data analytics to help our clients in the financial industry identify their risks and improve their business to attract customers and retain value.