RPA and AI—the next step in the efficiency game for banks
Blog: Capgemini CTO Blog
Banks have been seeking cost reduction strategies for years. Recently, they started using Robotic Process Automation (RPA) to further reduce costs and transition from services-through-labor to services-through-software. Ever since RPA was introduced to the financial world, this virtual workforce has helped banks minimize (or, in some cases, eliminate) human intervention in the execution of tasks and decision-making and dramatically improved operational efficiency, sometimes up to 70%.
RPA has been widely adapted by many financial institutions and other industries; but, if you think stand-alone RPA is the trendiest technology by far, you may already be already lagging.
Artificial Intelligence (AI) has started permeating intelligent organizations and advancing to the fundamental toolset for daily engagement with people for both customers and employees. At Capgemini, our definition of AI includes technologies such as speech recognition, natural language processing (NLP), semantic technology, biometrics, machine and deep learning (ML/DL), swarm intelligence, and computer vision.
The adaption of RPA and AI in the banking sector
Even though the most prominent examples of AI exist in the customer experience space, AI technology is also playing a major role in driving further operational efficiency across various sectors. Coupled with RPA, AI can replicate not only simple, but also complex, labor activities requiring expert judgement or complex decision-making at greater scale, speed, and accuracy than humans.
RPA has helped banks dramatically accelerate speed of work and adherence to working procedures in repetitive and manual-labor-heavy processes. However, if banks leverage the power of AI and the surging popularity of cognitive technologies on top of RPA technology, they will be able to lead the digital transformation and unlock untapped opportunities. They can use computer vision and DL to understand and act upon digitized documents, utilize ML to find the best solution to any unexpected event in a process, and closely monitor human transactions through NLP tools, prompting alerts for any out-of-the-ordinary activities. Through these and other kinds of applications, AI helps drive efficiency, reduce risk, and foster better compliance.
At JP Morgan, lawyers spent thousands of hours studying financial deals. Now, an AI system is doing the challenging job of interpreting commercial loan agreements, taking on a task that has swallowed 360,000 hours of work by lawyers and loan officers. The AI system reviews documents in seconds and is less prone to error. The system has cut down on loan-servicing mistakes, many of which resulted from human error, in interpreting 12,000 new wholesale contracts per year.
The future of Artificial Intelligence
AI is improving its capabilities with increasing speed thanks to better computing-power and specialized hardware, and has increasingly proven itself in historically human-dominated fields. In the future, AI will be able to autonomously analyze what’s out in the open digital world (internet), combine internal data and open data, and pursue ideas suggested by the AI algorithm. In the not too distant future, we may even see one AI solution creating another.
Today, the rise of machines and AI is no longer something straight out of a sci-fi movie. Robots will completely replace some human labor in the near future. Therefore, now is the time for banks to seize the opportunity to shift gear in the efficiency game and use AI to their best advantage.
Authors:
Jun Wang
Senior Consultant
Capgemini Consulting NA, Financial Services
Lars Boeing
Principal
Capgemini Consulting NA, Financial Services
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