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Similar to the largest US institutions, European banks have a strong interest in exploring the impact of AI on business functions. The US market, however, seems to be far more active in areas beyond virtual assistants and chatbots, while European banks are heavily focused on customer-facing interfaces. In fact, a study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor.
Banks are using chatbots and voice bots to interact with customers and resolve requests before a human intervention is required. Fortunately, the technology behind it – natural language processing and generation - will make it increasingly difficult for customers to tell whether they are talking to a human or an AI interface. Biometrics, particularly voice and face, could be used as authentication methods to ensure secure interactions.
What else are European banks doing with AI?
To mention a few examples – Russia-based Tochka Bank has launched a Facebook bot for a range of financial services that include allowing the bank’s clients to check their accounts, finding nearby ATMs using geolocation, calling the bank function, contacting customer support, and making payments via Facebook messenger.
Another major institution has integrated IBM Watson and has designed a customer service chatbot – a natural language processing AI bot to answer customer’s questions and perform simple banking tasks like money transfers. If the bot is unable to find the answer it will pass a customer over to a member of staff.
A London-based bank has deployed a text-based chatbot, which customers can use on the banks’ online help pages. The chatbot can answer 200+ basic banking queries.
With RPA expected to have a $6.7 trillion global economic impact and a global market potential standing at $8.75 billion by 2024, most institutions in the US have work planned to bring automation across functions. Europe also has its examples – a securities services function for a major institution in Europe is employing a trade-matching tool using artificial intelligence and predictive analysis to further automate the trade processing services it provides to investment managers.
Using predictive analysis, the solution analyzes historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. The bank is already making good progress, having reached around 98% prediction accuracy.
A tool developed by one of the largest European institutions systematically screens contracts for compliance purposes. It takes 15 seconds to screen 150 pages, and the tool makes it possible to identify the names of legal entities, people, locations, vessels, etc.
HSBC, for example, is bringing robots to help it spot money laundering, fraud, and terrorist funding. The bank is planning to integrate the AI software of Quantexa, a UK-based startup, to screen the vast amounts of data it holds on customers and their transactions against publicly available data, in the search for suspicious activity.
The same institution is also using AI to predict how customers might redeem their credit card points so it can market its rewards offerings more actively and effectively. The rewards program will read customer data to predict how they might redeem their credit card points so it can market the offerings of a certain category – travel, merchandise, gift cards or cash – more actively.
Another European bank has partnered with a startup and implemented its enterprise analytics solution using AI to better identify instances of fraud while reducing false positives. Through this, the bank reduced 60% of false positives and increased true positives by 50%.
ING Bank went ahead with replacement of a traditional rules-based anomaly detection system with one powered by machine learning algorithms. Previous testing has shown this will improve performance significantly – much more than the 5%-10% typically attained in a technology upgrade.
One of the largest European banks launched an artificial intelligence bond trading tool that will help human traders to swiftly gather better bond prices. The tool will use data from hundreds of thousands of trades to help the bank’s traders to get better bond prices faster. In a six-month trial, the AI tool led to faster pricing decisions for 90% of trades and cut trading costs by 25%.
UBS, for example, is working on solutions using machine learning to develop new strategies for trading volatility on behalf of clients. It scans vast amounts of trading data and creates a strategy based on learning from market patterns. The strategy, however, has then to be approved by human employees. The bank is also developing an AI tool for investment research. It can screen through market data through SEC filings and can actually do a company valuation with all of the inputs that a human analyst would use and can produce text in a fairly decent quality and almost human-mimicking language.
Deutsche Bank has rolled out new AI-based equities algorithmic platform in APAC, which was designed with a self-learning mechanism allowing its systems to predict equities pricing and volume with more accuracy, thereby enhancing the quality of execution. This was added as a capability to its Autobahn platform.“Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry.”
This article originally appeared at: Medici