Managers who work in the financial area, and UK-resident banks or investment companies, should always look for an optimal approach to customers, processes, and data. Embracing AI technologies may come in handy here. That’s why the AI maturity of UK banks and other financial institutions is in the spotlight. Let’s figure out what benefits these institutions get by implementing and scaling AI.
What does AI do exactly? AI finds dependencies and patterns that serve as a basis of insights related to marketing, customer retention, business processes, and operating activities. By using AI algorithms and big data, fintech companies can personalize their services. For example, banks develop unique apps capable of creating offerings tailored to customers’ needs, taking into account their location, age, spending behavior, and preferences. According to the State of the Connected Customer report prepared by SalesForce, 66% of customers expect just such an approach, where companies clearly understand their needs.
At the same time, the customer requirement is becoming more and more diversified. The evolving technology capabilities, including transcript analysis for mood prediction, enable fintech companies to find the appropriate solutions. In the future, fintech companies are going to fulfill customers’ wishes and create even more data-driven solutions with the help of high-quality data and complex AI models.
This transformation should be aligned with business objectives and restrictions. According to the BoE and FCA AI Public-Private Forum Report published in February 2022, financial companies have to consider AI from the perspectives of governance, data, and model risk. Taking into account all these perspectives, they can adopt AI much more smoothly, ensuring the provision of new opportunities for customer engagement, risk management, chatbot application, and so on. We’ll take a look at these opportunities in the next sections.
Anti-fraud tactics help fintech companies create safe experiences for their customers. With AI, financial services become more secure, since algorithms can identify anomalous behavior and prevent fraud. Let’s find out what we mean by frauds across the banking area.
The most widespread types of fraud are unauthorized transactions, phishing scams, and identity thefts. Fraudsters use these approaches to steal money. Statista reveals that by 2027, the total value of fraudulent transactions made with payment cards can reach $38.5B. So, to protect clients, fintech companies should use sophisticated AI methodologies, namely:
- Build customer profiles. Understand the typical behavior of customers via Machine Learning, categorize the profile, and predict the future behavior – all together this enables identifying suspicious transactions and requesting additional confirmation.
- Implement fraud score assessment. Based on the processed transactions and a dozen of other factors such as time of the transaction, IP address, amount of money, and so on, an algorithm analyzes the risk of fraud; after that, the transaction can be approved, rejected, or be forwarded for additional review.
- Investigation of fraud. ML assesses transactions, thus allowing teams to simplify time-consuming investigations.
- Additional verification. With AI, banks can apply ID verification, introduce facial recognition, or check fingerprints to conduct any financial transaction.
As for risk management, banks may use AI models to predict borrower behavior. It’s notable that the AI application in this area should be clear and explainable. We can look at the model which employs Shapley values and was created to find out credit scores and make relevant assumptions regarding borrowers. The Bank of England mentions that explainable AI here foresees the ability of a stakeholder to understand crucial drivers of a model-driven decision. Otherwise, the AI application may be considered discriminatory.
Chatbot or virtual assistant adoption is a prominent example of AI-powered fintech innovations. Because of the latest technology advancements, amazing applications have appeared. Let’s explore some of them.
Cleo is a personal financial assistant who aims at encouraging people to save money. The product of the London-based company has become popular among users under 35 who want to track their spending and save automatically. ML-powered app targets Gen Z and includes various features, namely different saving styles, a credit coach, and salary advice.
Kasisto is an AI chatbot for the financial department. It can be suitable for companies working in industries like retail or wealth management. Kasisto may be perceived as a booster of digital customer engagement. The platform allows institutions to assess conversational data and train models to improve user experience. However, Kasisto isn’t one of a kind. Similar chatbots are Tars, Haptik, Hybridchat, Core, and even Paypal (yes, this company also has an AI-powered chatbot that runs via Facebook messenger).
Nova is a tool that predicts bills, expenses, and the achievement of financial goals. With its help, users can manage their balance, avoid impulse purchases, and contribute to their financial well-being through literate planning. Nova has also a plan for enterprises that want to motivate their staff and help them with financial planning.
To remain competitive in the market and meet their customers’ needs, fintech companies, which work in banking, payments, investments, insurance, blockchain & cryptocurrencies, actively use AI and ML. The accuracy and convenience of these solutions enable famous UK companies to benefit from them.
Tradeteq has been working as a tool for banks and institutional investors since 2016. The product was created to implement automation for reducing corporate credit and trading finance transaction costs. This approach also allows financial institutions to find new opportunities for investments and increase the financing of businesses.
Monzo & Revolut
Monzo is a digital bank that has already implemented AI capabilities into customer service. The internal system not only helps customers to find relevant answers to their questions but assists customer support agents to choose relevant responses to queries. Monzo has more than 2.5 million customers in the UK.
As for Revolut, this fintech company with a global money application uses AI technology to avoid card fraud. An ML system analyzes customer behavior to detect anomalies and prevent suspicious transactions.
The London-based fintech company named Zilch launched a beta version of their product nearly three years ago. It enables clients in any place where Mastercard is accepted to buy from Zilch’s partners by using interest-free, six-week payments. Zilch has become popular with Gen Z and millennials.
Between 2016 and 2020, UK-based fintechs received $111B in investment across 1,380 venture capital, private equity, and M&A deals, according to Global City. A large investment and AI capabilities sound like a very appealing future. Even more scalable products and fintech startups will appear in the coming years since this industry attracts both bold ideas and stable funding.
As a result, employees and customers of financial services will be able to work and cooperate more productively. Also, in this race of high-ranking Fintech ecosystems, London will definitely be able to keep one of the leading positions.
By Maksim Bieliai, BA Team Leader and Fintech market analyst at MobiDev