Artificial intelligence (AI) is one of the most impactful technological revolutions the world has witnessed. Today, customers today are increasingly exposed to advanced technologies such as AI-enabled chatbots and intelligent voice assistants like Apple Siri, Google Assistant, and Amazon Alexa, making personalization a high priority for incumbent banks.
Today, AI enables financial institutions to solve many critical problems, thereby saving money and increasing the efficiency of the workforce. By deploying AI-based solutions, banks can improve the outcome in various dimensions such as customer service, risk management, cross-sales, etc. A research study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, and India) found that 27 out of these 34 banks have implemented AI in their front-office functions in the form of a chatbot, virtual assistant, and digital advisor.
The capability of AI is enormous when it comes to enabling transformational business outcomes and bringing exceptional values to banks. Intelligent automation has the ability to replace repetitive manual tasks, improve customer experiences, and develop customized products, thereby driving the growth, profitability, and sustainability of the banks.
Worldwide spending on cognitive and AI systems was estimated to reach $19.1 billion in 2018, an increase of 54.2% over the amount spent in 2017. With industries investing aggressively in projects that utilize cognitive/AI software capabilities, IDC forecasts cognitive and AI spending to grow to $52.2 billion in 2021 and achieve a CAGR of 46.2% over the 2016–2021 period.
According to IDC, much of the $3.3 billion spent by the banking industry in 2018 went toward automated threat intelligence & prevention systems, fraud analysis & investigation, and program advisors & recommendation systems. The cognitive/AI use cases with the largest spending totals in 2018 included automated customer service agents ($2.4 billion) with significant investments from the retail and telecommunications industries; automated threat intelligence & prevention systems ($1.5 billion) with the banking, utilities, and telecommunications industries as the leading industries; and sales process recommendation & automation ($1.45 billion) spending led by the retail and media industries.
Banks are actively exploring AI deployment opportunities under the massive pressure to compete on efficiency and customer experience. For example, JP Morgan’s COIN (Contract Intelligence) does the job of interpreting commercial-loan agreements that, until the project went online in June 2016, consumed 360,000 hours of work each year by lawyers and loan officers. The software is less prone and reviews documents in seconds.
According to Nasdaq, in 2018, JPMorgan Chase had a $10.8-billion tech budget, with $5 billion set aside for new investments. JPMorgan’s treasury services division handles an average of $5 trillion daily in everything from payroll and remittances to multi-billion-dollar merger checks, and the bank wants to bring AI into this game. The bank was teaching its machines about its clients so AI could start anticipating their questions and needs. The Bank of America has also made its AI debut with Erica, who leverages predictive analytics and cognitive messaging to provide financial guidance to over 45 million customers.
Deploying AI will undoubtedly drive both cost and operational efficiencies for banks. However, ensuring the right quality of data, an adequate understanding of AI’s inherent risks and regulations will lead to a successful implementation of AI.
Successful implementation of AI calls for continuous investments in evolved technologies. Banks might end up losing grip of their customer value proposition to non-financial tech giants if they fail to keep pace with the evolving technological trends. AI will strengthen the relations between banks and customers by addressing the issues in real time and customizing the solutions for the individual customer.
Intelligent automation enables banks to reach new stages of maturity in technology and innovation. Successfully scaling to digital banking requires a significant shift from a manually driven process to more of a data-driven mechanism, triggering operational efficiency in the whole spectrum of banking businesses. The integration of Robotic Process Automation (RPA) with the current applications and systems drives growth productivity, digital transformation and enables quick ROI by automating the repetitive and manual-based processes.
RPA is expected to have a $6.7 trillion global economic impact, a plan that will result in a 40–45% growth of global spending on technology. In addition, the estimated global market potential of RPA stands at $8.75 billion by 2024.
The vast amount of customers’ data is something banks should leverage to gain a competitive edge over the non-financial tech giants. Customers are now allowing banks to use financial data to avail more and more personalized services. The capabilities of AI solutions will grow exponentially with the availability of a vast amount of customers’ data.
Last but not least, banks should empower the in-house workforce to build AI-enabled technological capabilities by conducting training sessions regularly. Employees need to get their hands on AI-enabled technologies, driving higher productivity in terms of customer engagement and operational efficiency. Delivering personalized services at a competitive price to customers will require banks to map the upskilling of the workforce.
For example, a robo-advisor can connect a potential SME client to the relationship manager for more information about the banks’ products and support to browse and operate applications for new products and services. Not only that, but a robo-advisor can also give analytics-driven information (related to lifestyle, preferences, etc.) to the client. The real-time information will help the relationship manager be well-informed about its client before starting their first conversation, making the onboarding process seamless.
Leveraging AI cannot be considered an option anymore but a necessity to transform the legacy infrastructure. It’s high time for banks to undergo a restructuring process on how they have been working, operating, and skilling the workforce. However, a set of guidelines need to be in place to ensure the ethical use of the technologies.
Using an intelligent framework will lead to unlocking the value and redefining the operational mechanism of banks. Though the implementation of AI involves the hassle-filled task of hiring expensive human resources with expertise in AI and restructuring the legacy system, banks cannot afford to let go of the digital transformation it will bring in the premise. Brooding over the hassles of deployment will not add value, but focusing on minimizing the ill effects and embracing the benefits will yield positive outcomes. The need of the hour is to develop a roadmap on leveraging the capabilities of AI to the fullest.
Most incumbent banks now seemed to have started taking multiple steps towards integrating AI as a part of their strategic goals. More than a threat, AI exhibits the opportunity for people to develop new skill sets and embark into the era of an AI-driven automated world.
By putting together the right skilled people, AI-enabled technologies, and amplifying personalization for customers, banks can sustain their leading position in the market and gain a competitive edge over non-financial tech giants.
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