Digital Transformation is a new movement sweeping across many industries, but banking as a sector is particularly impacted. Artificial Intelligence (AI) is a key driver in digital transformation. This blog will give you a summarised perspective on the impact of AI in banking.
What is driving AI adoption?
Here are some of the central trends driving the adoption for AI in banking:
- Deluge of data
- Compliance & regulation
- Changing customer behaviour
- Pressure on margins in all business operations
In addition to this list, competition between banks for customers is intensifying. The FinTech firm FICO commissioned research which identified that Millennials (25-34 year-old age group) are 2x likely to switch banks in a single year. This customer churn is important as the average Millennial will only use banks offering online-only services; and holds 6.27 financial products, compared to 5.6 products for the entire US adult population.
The result is a need for banks to efficiently handle vast amounts of data across all lines of business in real or near time and adopted for online/mobile banking. AI offers a means to solve this and the adoption of AI by banks is accelerating fast, supported by aggressive development and research from technology vendors such as Intel, IBM and the banking software firm Intellect Design.
What is AI?
The phrase Artificial Intelligence was first coined in 1956 by John McCarthy, an academic at Dartmouth College, USA. Since then many definitions have arisen but the one I like the best is a simple explanation, namely:
The capacity of a machine to imitate intelligent human behaviour
(source here)
But if the definition is simple, the implications are not. AI is developing into a platform whereby computers seek to create better services and products, NOT to achieve a perfect replica of the human mind.
Examples of AI success in banks
JPMorgan Chase recently launched a Contract Intelligence (COiN) platform based on AI. JPMorgan Chase calculated that reviewing 12,000 commercial credit agreements took 360,000 human hours. The COiN platform reviewed these thousands of credit agreements in seconds. The successful AI innovation trials led the bank to set aside $3bn for future technology initiatives aimed at developing new and enhanced digital and mobile services.
In February 2018, Wells Fargo announced the establishment of a new Artificial Intelligence Enterprise Solutions team, positioned within its Payments, Virtual Solutions and Innovation group. By April, Wells Fargo were able to launch a pilot of an AI-driven chatbot through Facebook Messenger. This virtual assistant communicates with users to provide real-time account information and help customers reset their passwords.
BNY Mellon has invested heavily in robotic process automation to improve the efficiency of its operations and reduce costs. The results have been impressive:
Bank of America has had success with AI chatbots to leverage predictive analytics and cognitive messaging to provide financial planning guidance across its 45 million customers. The results have contributed to record profitability for the bank.
- 100% accuracy in account-closure validations across multiple systems
- 88% improvement in processing time
- 66% improvement in trade entry turnaround time
- ¼-second robotic reconciliation of a failed trade vs. 5-10 minutes by a human
Best practices in adopting AI in Banking
AI is a source of innovation across all banking operations, from risk, corporate and retail. To get the most from AI, two things must happen:
- Partner with the right vendors
Have dedicated AI teams in vertical and horizontal banking operations – the vertical teams look after the AI applications in areas such as risk, corporate and retail banking, the horizontal teams facilitate the engagement of AI, such as engaging 3rd parties, hiring staff, governance.
Create centres of excellence – these should be multi-functional, business-led and avoid being reactive. Special focus must be made of engaging with the back office divisions of a bank who traditionally are often left behind or the last people in the loop. A natural proclivity of centres of excellence is that they often start centralised but become dissolute. This is not a drawback, in fact the spread of AI across the business units can offer a competitive advantage to a bank. It also avoids technologists living in ivory towers.
Have innovation forums for AI – led by a business leader and able to cover front, middle and back office. Ideal format would be a banking leader, COO or equivalent, technologists and relevant external partners.
What are the challenges of AI in banking?
Techies vs. bankers AI innovation can be handicapped by the natural (and compliance-led) silos that exist in a financial organisation. The organisation structure and culture of banks can be exasperated by the two tribes that exist inside a bank – the technologists and the business. There is often a disconnect between the technology and business divisions. Therefore, strong leadership is needed break down these silos or else the benefits/return on investment of AI are diluted or slowed.
Leadership & culture The culture of a bank towards innovation and inter-division trust will enable or inhibit AI benefits – success needs strong leadership from the top. Also, the culture must allow willingness to try things out and fail.
Scale is also important – the most successful AI innovations are appearing in the larger banks who have higher levels of budget and resources.
Impact of AI on Jobs
AI will augment not replace jobs? There are fears about AI replacing employees. The common response amongst banks is that AI “augments, not replaces” staff. I believe this is disingenuous – AI will impact banking jobs, augmenting some but replacing many. Research by the Bruegal Group suggests 60% of all jobs will be impacted by AI. Most large banks still have thousands of employees performing mundane paperwork and legacy processes, many of these occupations will disappear, replaced by AI. This will be repeated in other staff-heavy industries (e.g. law and accountancy).
Race for talent
There is a limited number of experts in designing and developing successful AI platforms. Banks face a challenge in hiring and retaining people with skills in machine learning and AI. Companies like Amazon, Google and Facebook not only have a more alluring reputation for innovation, but they also have much higher profit per employee ratio (Fun fact: Facebook makes $750,000 in profit per employee), allowing tech firms to shower top talent with higher salaries and excellent perks.
Conclusion
AI is a strategic priority for many banks to offer better service for their customers, improve performance and increase profitability. Key drivers for AI adoption is competition for customers and the data deluge – the deluge needs taming and banks will leverage AI to for their key strengths of data-rich assets and trust.
But AI is still costly and needs strong leadership at the top. The people-side of AI strategies will also be critical on two levels – competition for the best AI developers and removal of front and back office jobs. Although the PR departments of banks will highlight innovations in the use of chatbots, however IT budgets will focus on business intelligence and cybersecurity.
In summary, AI will drive disruptive and strategic changes that banks (and indeed all modern economic organisations) will have to prepare themselves for.
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Useful links:
Interview with Alexander Fleiss of Rebellion Research highlights the serious concerns of AI-related job loss and automation in the finance sector.
For a deeper look at the origins and developments of AI in finance, see this interview with Brad Bailey of Celent Securities and Investments
Other great resources include FICO’s ebook on digital trends in customer behaviour and Intellect Design’s research on Next Generation Banking Experience
