By Parth Desai ,Founder & CEO of Pelican
Artificial Intelligence (AI) is already a ubiquitous part of our everyday lives. Think of asking Siri a question or having your car automatically manoeuvre to park itself – more of our daily devices rely on AI disciplines to apply interpretation and understanding to give information context, and thereby learn and act.
The use of AI is growing and there has been much debate about its use by banks to streamline processes and add value, some of which we are already seeing in the form of robo advisors and big data processors. AI has the potential to address many of the challenges and expand opportunities for banks – reducing middle and back office administration, for example.
To better assess the potential application and benefits of AI for financial institutions, we first need to clarify what we mean by artificial intelligence and exactly how it is different from ‘traditional’ technology.
AI uses knowledge and computing power to simulate intelligent human behaviour. The complex human abilities of perception, mobility and interpretation are skills that require analysis such as understanding, natural language and vision; these are the building blocks of AI.
One such building block is Natural Language Processing (NLP). This enables computers to understand free-form human language, to analyse then act upon this information automatically. Another key discipline relevant to banks is Machine Learning, where experiences and insight can be gained from previous behavioural data without being explicitly programmed to respond in a pre-determined manner.
By contrast, traditional banking technology is essentially dumb. Processes and services are done through layers of software programmes – from the microprocessor core, up through the various operating and application systems to a customer’s mobile app interface – with each layer designed to perform a very specific and predicable task.AI technology differs in its ability to place context into situation – to mimic the biological processes of the human brain to remember and learn, developing understanding and knowledge – and thereby change behaviour and actions.
Banks can benefit from intelligent knowledge-based and learning technologies.Natural language processing AI disciplines have been utilised in transaction banking since the 1990s. The subsequent emergence of big-data and cloud computing saw the adoption of Machine Learning capabilities, with Fraud detection being a more recent addition to the practical application AI within the banking sector.
There are several challenges facing financial institutions that AI can play a significant role in addressing. The common uses of AI in transaction banking today tend to be task-specific. Where AI has real benefit for banks however, is its ability to solve highly complex and everyday problems in far less time than either humans or ‘traditional’ technology possibly could. This high velocity of ‘understanding’ gives AI strong commercial advantages in three broad banking areas: compliance and fraud prevention; process efficiencies; and product development. Let us look at each in turn.
The most recent AI banking and payments implementations have been made by tier-one financial institutions in the area of AML sanctions screening and fraud prevention. The growing threat to banks posed by illegal transactions and payments fraud should require no explanation here. It is not surprising that the enormous advantages of applying AI disciplines to compliance and security have been recognised and adopted by some of the most forward-thinking of financial institutions.
The deployment of machine learning and other AI disciplines are proven and powerful AML, Sanctions and Fraud Prevention tools that can provide real-time validation and authentication of payment transactions. This AI approach does not merely respond to past patterns of money laundering or fraud, but deploys context-aware ‘understanding’ and anomaly aware capabilities – detecting and thereby preventing fraudulent transactions in real-time.
The Tokyo Stock Exchange has confirmed that it is deploying machine learning AI tools as the market surveillance solution to investigate potential illegal trading practices. In tests, the AI robots have proven highly accurate in assessing potential suspected activity.The machine learning tools employed are able to learn from vast amounts of data and make independent judgments, without the need for compliance staff to have set up hypotheses in advance. This enables the AI compliance system to detect highly complex trading breaches that humans have not even conceived.
Similar AI compliance and fraud prevention systems are being utilised today by individual financial institutions and we should expect adoption rates to increase. But there are other banking functions and activities that can equally benefit from the unique strengths of an AI-based approach.
Despite significant investments in back-end processing and compliance, many banking systems – specifically the areas of payments processing, repair, routing and investigations – remain highly inefficient. It’s not the payments themselves that are changing, but the usage, integration and user interface demands.
The context learning and natural language processing capabilities of AI-based payments systems have over the past two decades been proven to dramatically increase straight through processing rates, enable intelligent least cost routing, and result in the removal of inefficient manual interventions and repairs – though these AI benefits have largely been the preserve of larger transaction banks. In an environment where cost reduction remains a priority and the competitive landscape is changing, no bank can afford to ignore the power of AI to help realise the vision of full automation required to complete the transformation of the payments business in this digital age.
Speed to market
It is the intensified demands of the 24/7 digital economy that shape the third distinct area of potential commercial advantage. Arguably the greatest opportunities for banks to leverage the capabilities of AI are in addressing the myriad of operational and business pain points experienced in product innovation and time to market.
The vast and invaluable amounts of data banks possess on customer behaviour and preferences can be exploited via machine learning technologies. This allows invaluable insights to be gained and new, more relevant products and services, to be created.
The use of a knowledge-based approach in combination with the right AI engines can also help ensure time to market is significantly reduced. This will help banks to compete more effectively against the fintech players and new market entrants.
AI and banking
Looking further into the future, the use rich and intelligent interface technologies like voice recognition and natural language processing will be a powerful combination to increasingly enrich and change the way humans interact with machines with omni-channel application. AI will be the new type of user interface and will tremendously enhance the user experience. For example, the recent introduction by a challenger bank of voice-based account management and payment initiation shows how AI innovation can significantly improve the user experience.
Banks and financial institutions already using AI-based solutions have been able to reduce, or virtually eliminate, the high levels of human intervention and manual processing that were previously necessary. Other areas also benefiting from AI-based systems include automation of exceptions, investigations and customer retention.Whether utilising the power of natural language processing to fundamentally transform the way customers interact with the Bank, or leveraging the data insights, speed of deployment and increased sales opportunities provided by machine learning technology, AI can enable Banks to achieve lower costs, increase revenue, accelerate processing time and reduce errors across the board.
The advantages to banks of adopting AI-based disciplines are proven and can be profound. Can you afford to ignore the benefits of AI?
“Original publication in Finance Digest Issue 1 https://www.financedigest.com/