AI today can be described in terms of three application domains: cognitive automation, cognitive engagement and cognitive insight.
Cognitive automation: In the first AI domain are machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools to develop deep domain-specific expertise and then automate related tasks.
Cognitive engagement: At the next level of the AI value tree lies cognitive ‘agents’: systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow to detect real time key patterns and relationships from large amount of data across multiple sources to derive deep and actionable insights.
Here are five key applications of artificial intelligence in the Banking industry that will revolutionize the industry in the next 5 years.
AML Pattern Detection
Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately.
HSBC has partnered with Silicon Valley-based artificial intelligence startup Ayasdi to automate some of its compliance processes in a bid to become more efficient. The banking group is implementing the company’s AI technology to automate anti money-laundering investigations that have traditionally been conducted by thousands of humans, the bank’s Chief Operating Officer Andy Maguire said in an interview last week.
Chat bots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots.
The State Bank of India (SBI) on Monday announced SBI Intelligent Assistant (SIA) — a chat assistant aimed to address customer enquiries like a “bank representative” does. Developed by Payjo, an artificial intelligence (AI) banking platform, “SIA” is equipped to handle nearly 10,000 enquiries per second or 864 million in a day — which is nearly 25 per cent of the queries processed by Google each day.
Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is carried out by automated artificial intelligence systems. Most of these hedge funds follow different strategies for making high frequency trades (HFTs) as soon as they identify a trading opportunity based on the inputs.
A few hedge funds active in AI space are: Two Sigma, PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel, Voleon, Vatic Labs, Cubist, Point72, Man AHL.
Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.
Mastercard announced the acquisition of Brighterion. Brighterion’s portfolio of AI and machine learning technologies provide real-time intelligence from all data sources regardless of type, complexity and volume. Its smart agent technology will be added to Mastercard’s suite of security products already using AI.
Recommendation engines are a key contribution of artificial intelligence in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines have been very successful and a key component in revenue growth accomplished by major banks in recent times.
With Big Data and faster computations, machines coupled with accurate artificial intelligence algorithms are set to play a major role in how recommendations are made in banking sector. For further reading on recommendation engines, you can refer to the complete guide of how recommendation engines work.
JPMorgan, which is spending big on technology as it looks to cut costs and increase efficiency, last year launched a predictive recommendation engine to identify those clients which should issue or sell equity. And now, given the initial success of the engine, it’s being rolled out to other areas.
Strategic Challenges of AI
As with any new endeavor, there are several challenges associated with the development and application of AI solutions.
Most banks and credit unions are in the early stages of adopting AI technologies. According to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.
Also, one of the biggest challenges is finding the right talent. With only slightly more than half of survey respondents (55%) stating they have identified an AI leader within their company, more than half of those have appointed the head of innovation as the leader.
In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.
12% of the overall group weren’t using AI yet because they felt it was too new, untested or weren’t sure about the security.
There is no clear internal ownership of testing emerging technologies— only 6% of those surveyed having an innovation leader or an executive dedicated to testing new ideas and processes.
How to make AI Part of Banking Ecosystem
The potential of open banking and artificial intelligence are intertwined, making up the foundation for a new banking ecosystem that will most likely include both financial and non-financial components. By partnering with fintech providers and data analytic professionals, the power of organizational data and insights can be realized. The partnerships and structure decided upon today will determine an organization’s competitive differentiation in the future.
Multiple providers are offering AI-based solutions and, as a result, banks need to navigate between specialist players and AI powerhouses. The goal will not to become more automated and less personalized, but to use technology and customer insights to become a lot more personalized and contextual.
The banking industry is still in the early stages of developing strong AI solutions. While these solutions can impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience. Singaporean bank DBS had the vision to launch Digi bank, India’s first mobile-only bank. Being paperless and branchless, Digi bank had to rely on emerging technologies like conversational AI to succeed. Digi bank was built with one-fifth of the cost of a regular retail bank and can contain 82% of customer inquiries with bots. Some banks just want to hand off responsibility to the vendor but Digi bank’s approach is to empower the customer with self-service tools. They don’t want to be professional services
There are four key recommendations that experts make to financial services firms who are looking to effectively exploit the value of AI. These are:
Look to invest, learn and pair up with experts from outside of the industry
Make use of cognitive computing to make better use of data
Implement the right mix of platform technologies
Strive to maintain a human touch.
In conclusion, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend. This trend is likely to grow exponentially in the future. Companies that embrace this trend are likely to be winners