It is not the strongest or most intelligent units that will survive, but those who most rapidly adapt to change. ” Charles Darwin said almost 200 years ago. And although this idea originally referred to evolutionary processes, today it may as well be a commentary on changes in the financial market.
AI and Machine Learning are technologies that have been increasingly used in banking in recent years. Banks aware that they must develop technologically to remain competitive, are turning to solutions that will ensure their growth. According to Accenture data, by 2035, AI could double its annual economic growth rate, contributing to the evolution of working methods and building new relationships between man and machine. In addition, forecasts indicate that artificial intelligence will increase the efficiency of enterprises by up to 40 per cent and enable employees to use their working time more efficiently. Undoubtedly, several functionalities in the financial industry can be optimised based on AI mechanisms, so we decided to take a closer look at them. That being said, we chose 5 key areas that we believe will soon be dominated by AI:
Customer Service
Although it is difficult to imagine customer service completely devoid of the human factor, AI modules can be effectively used primarily in the area of process automation and contact channel management. Automating repetitive banking processes that do not require excessive verification does not only translate into considerable savings in the long run, but also reduces the number of mistakes made, which are an integral part of manual handling. Artificial intelligence implemented in customer service processes also means increased efficiency and transparency of activities.
Due to changes in communication models, i.e. the transition from traditional channels to those in which customers communicate with brands (social media, instant messaging, mobile applications), the number of channels banks have to operate in order to contact their customers is growing every year. The solution to this does not necessarily lie in increasing the resources of customer service departments, but also, and perhaps above all, in implementingan artificial intelligence module, for example, in chatbots or various types of virtual assistants. A well-configured chatbot can handle far more standard inquiries and problems faced by customers, and most importantly, it is available almost immediately after the customer initiates contact.
Customer Experience
Artificial intelligence can also serve as a support in building the customer experience. The analysis and interpretation of data allow for even greater personalisation not only in terms of the offer but also the contact with the brand itself. User-specific content and high availability of services are just some of the elements of a good CX. Thanks to behavioral analyses and statistics generated in real-time, banks can more accurately conclude about customer needs. In addition, artificial intelligence helps to soptimise the customer’s path – the touchpoint analysis helps to identify problems that may affect the customer’s “purchase” decisions. According to the IDC report, artificial intelligence can optimise processes at almost every stage of the customer’s contact with the bank, especially in the following areas:
- Advertising, marketing and engagement processes at the brand-customer interaction stage. . It allows you to better understand the consumer and provide them with tailored, unique and personalised services.
- Interaction with the consumer to provide additional information digitally and to support and help employees in cooperation with the client
- Direct and indirect customer and business support, getting the best value out of your transaction and resolving any problems or errors that may arise.
- Better understanding and supporting the relationship between the client and the company, primarily through data analysis and interpretation
- Focusing on customer characteristics through the use of artificial intelligence.
- Analysis of consumer-related data collected to better understand their needs.
Consulting (robo-consulting)
An interesting area in the implementation of artificial intelligence seems to be robo-consulting, i.e. automatic investment advice for clients. It consists in the fact that artificial intelligence learns the client’s needs on the basis of databases and proposes dedicated investment strategies to him, then manages the assets until a specified profit is obtained. Robo-advisers also enable full automation of some asset management services and online financial planning tools. By analysing several historical data, they can make better predictions about the behaviour of investment portfolios. At the same time, they help customers make more informed spending and savings decisions based on behavioural analysis. Although Poland currently lacks relevant legislation governing such services ; their potential has already been recognised by the Polish Financial Supervision Authority, thus preparing a draft establishing a formal position on robo-consultancy. As we read on the KNF website: The draft document is to comprehensively address the most important issues related to the conduct of robo-advisory services, which should be taken into account in the activities of a supervised entity. The draft position also applies to the entire process, from the service design phase to its practical implementation and monitoring of existing solutions. The position will aim to ensure uniform implementation of robo-advisory services by interested financial institutions, while taking into account adequate protection of clients, especially non-professional investors.
Data processing
Due to their specific nature, banks process huge volumes of data daily. In this context, there are two challenges: how to do it quickly and how to extract the maximum amount of information from the data. AI addresses the problem of high performance and speed, and also allows for high-level inference based on highly advanced analyses resulting from machine learning. Robots based on cognitive technologies related to the development of artificial intelligence can analyse the content of correspondence with customers, verify the correctness of complex loan documentation, behavioural segmentation based on actual financial behaviour of customers or even consultancy.
Cybersecurity and fraud detection
AI is primarily used for customer identification and fraud prevention in online banking. Credit card fraud has become one of the most widespread forms of cybercrime in recent years, driven by the massive increase in online and mobile payments.
To identify illegal activity, artificial intelligence algorithms validate customers’ credit card transactions in real-time and compare new transactions with previous amounts and the locations from which they were performed. The system blocks transactions as soon as it notices any potential risk.
To fight fraud, organisations are also increasingly using biometrics, which enables the recognition of people based on their physical characteristics. This method involves verifying users before they log into the system, based on, inter alia, fingerprints, iris or face shape (so-called face recognition).
Modules such as AML, Anti-fraud, KYC, supported by artificial intelligence, allow to significantly reduce the risks and losses related to financial fraud, which is reflected in the activities of organisations exposed to this risk. According to the Anti-Fraud Technology Benchmarking Report, by 2021, as many as 72% of organisations plan to implement automated monitoring, exception reporting and anomaly detection systems, and more than half of the respondents intend to implement solutions in the area of predictive analytics and modelling.
Summary
The future of the banking industry in terms of the use of AI and Machine learning is extremely intriguing. While the progressive automation of the banking industry and greater openness to new technologies, on the one hand accomplishes the enormous potential of this service area, on the other, it opens the door to new threats and cybercriminals. That is why it is so important that the execution of tasks related to the implementation of artificial intelligence and machine learning takes place in accordance with good practices and with the participation of experienced technology and business partners.