The financial sector has been witnessing a huge propensity of investment in digital transformation with several financial institutions already leveraging the benefits of personalized recommendations. For instance, the Commonwealth Bank of Australia (CBA) has been successfully leveraging AI-based decisioning for personalized customer engagement in home loan lending, while, Royal Bank of Scotland (RBS) has integrated personalized recommendations into its web, branch, contact center, and outbound channels, for real-time intelligence and guidance they need to initiate high-value conversations.
Moreover, some of the leading global financial institutions such as JP Morgan, Bank of America, and Barclays, are heavily investing in a wide range of digital portfolios and rapidly enhancing their technological acumen for enhanced customer experience. JP Morgan Chase has taken a step forward in this leap with a staggering $9.5 billion allocation of funds (planned budget) for technological investments. The company has taken significant initiatives over the past few years to leverage the growing opportunity through digital initiatives, some of which are showcased below –
- 2015: Launched machine learning driven Emerging Opportunities Engine used as a predictive recommendation to “identify clients best suited for follow-up on equity offering” and post its success, planned expansions were introduced for other application areas (such as debt capital markets) based on predictions of client financial data, issuance history, and market activity.
- 2016: Established a center of excellence (within its Intelligent Solutions group which drives innovation across the firm by leveraging big data and advanced analytics such as machine learning)
- 2016: Contract Intelligence (COiN) platform was rolled out as a pilot project. The platform uses machine learning to review thousands of legal documents and deliver a proper interpretation of the clauses and critical data points gathered from it.
- 2017: Planned roll-out of its virtual assistant technology, termed as “cognitive automation”, which is powered by natural language processing interface, to help in assistance in applications such as service tickets and thereby automate about 1.7 million requests annually. This was set with targets of at least $1.3 million in savings annually.
- Future (2018 and beyond): Plans on increasing use cases of machine learning to applications such as anomaly detection for fraud and cyber threat prevention, thereby creating a secured and optimized roadmap for targeted-trading strategies and client servicing channels.
Eventually, it is evident that most financial institutions will follow suite, with some of the key focus areas the digital initiatives being personalised recommendation, fraud detection and risk assessment, digital assistance and customer segmentation & positioning.
– Shiladitya Chaterji,
Senior Analyst– ICT,
Artificial Intelligence and Cognitive Systems In BFSI – By Technologies (NLP, Machine Learning, Deep Learning, and Image Processing & Video Recognition ), Deployment Types(On Premises, and Cloud), Verticals (Banking, Financial Services and Insurance), and Regions (Americas, Europe, Asia-pacific, and MEA): Global Drivers, Opportunities, Trends and Forecasts to 2022