7 Easy Ways to Make Artificial Intelligence in Banking Faster

1. 7 Easy Ways to Make Artificial Intelligence in Banking Faster Machine Learning and Big Data are altering the traditional banking processes, making them available 24 x 7 in an intuitive and user-friendly interface like never before. Modern banks and Fintech giants have started using AI to turbo-charge various financial applications. According to a report by PricewaterhouseCoopers1 in 2017, AI and ML investment in the Indian Fintech landscape embarked on an upward ascent from $ 4 billion in 2015 to $ 5.1 billion in 2017. #1 Better Standardization and Analysis of Data Banks are reservoirs of customer data, but the biggest handicap is that many institutions have not mastered the art of number crunching and synthesizing data. Data collection, analysis and inferences must be streamlined and integrated, since AI lives and breathes data. #2 Enhance the pool of Substantive knowledge in ​AI with expert resources AI implementation requires specialized knowledge in all disruptive AI-backed technologies like robot technology, process automation, cognitive hypothesis generation and predictive analytics. Special efforts must be made to the source, retain, train, develop and incentivise AI-capable human talent pool. #3 Extending AI power to the value chain in full While banks were super-active in developing flashy live chat assistants and a responsive front-end, AI implementation across the mid blockchain advisors, chatbots,

2. and back end have been relatively slow. Launch AI across the entire value chain, right from RPA (robotic process automation) to real-time fraud prevention. #4 Creating an environment conducive to Continuous Learning AI is the means to an end. Personalized customer service is an end that is brought about by AI. The overall AI strategy must be ​innovative and accommodative. Only then the AI potential of banks can be leveraged and maxed. #5 Deploy AI in risk management One of the major concerns of banks to move to the AI format is that of cybersecurity. Regulatory compliances have become water-tight and AI applications in banking can be faster if it is not chained by the legacy information systems. Providing next-gen security to the banking sector is the rosiest promise of AI. #6 Incentivizing Discovery of New AI use cases For AI to bring tangible results to the table, brainstorming different and new uses to AI is imperative. For example, AI can be of immense use in wealth management, hedge funding and credit rating. #7 Making critical and informed AI-powered decisions According to Accenture's 2018 Banking Technology Vision Report2, over 93% of bankers in India opine that AI tools are increasingly used to make data-driven and automated decisions. Scaling the AI infrastructure becomes rapid-paced only when insightful and fact-based decisions are made. Chalking a clear roadmap in the form of a full-fledged AI implementation strategy is core to inundate your bank's core competencies with the AI advantage. The thumb rule is that artificial intelligence must co-exist and complement human intellect.