“Organizations that invest boldly in becoming more data-driven – by developing the right data management platform and a clear data analytics strategy − can be winners over the long term…”.
The most effective use cases through Machine Learning (ML) algorithms are: 1) improve the customer experience and drive growth: a) deliver personalized services based on customer profiles; b) recommend the next-best product to buy to accurately cluster customers and prospects into segments according to probable needs; c) provide robo-advisor services, which help customers with investment decisions; d) automate personal finance management to give customers a holistic view of their finances; e) offer chatbots, they provide predictive messages and automate tasks such as money transfers or balance inquiries. 2) Optimize risk controls and business outcomes: a) provide early warning predictions; b) predict risk of loan delinquency and recommend proactive maintenance strategies; c) improve collection and recovery rates; d) predict risk of churn and recommend proactive retention strategies to improve customer loyalty; e) detect financial crime such as fraud and counter-terrorism financing activities. 3) Automate business processes: a) algorithmic trading deliver subsecond timing advantages in automated trading; b) customer credit risk evaluation uses customer data for automated real-time credit decisions; c) customer complaint management uses data to understand why customers complain; d) inquiry response employs data to automatically route and respond to inquiries. 4) Improve operational efficiency: ML can predict operational demand based on historic data and future events. 5) Self-service analytics for everyone: using self-service analytics can unleash innovation, create organizational enthusiasm for using data insights, and develop new ideas on monetizing existing data assets.