The financial industry was one of the first industries to adopt and use the strengths of artificial intelligence. The annual budgets of large banks, which amount to billions of euros, can be compared to the national budgets of some developing countries. It is therefore expected that banks and financial institutions in particular will be the main drivers for AI research and development in FinTech. They will also help bridge the AI knowledge gaps in other industries and support the ecosystem of FinTech startups .
The largest and most successful credit organizations already have official, well-developed AI strategies.
Most of these strategies require founding internal or outsourced AI departments or teams. The recently published Autonomous Research predicted that the use of AI technologies would enable banks to reduce operating costs by 22% by 2030. Over $ 1 trillion of today’s financial services cost structure is being replaced by machine learning and AI.
At the same time, large banks face a dramatic problem: They lack highly qualified AI developers, researchers, practitioners and data analysis specialists. The lack of professionals is causing technology development to slow down in many industries, with FinTech being the most damaged.
The previous stage in the distribution of FinTech startups and customer applications in the field of financial services was determined by smartphones. Around the same time the term itself appeared. Smartphones enabled FinTech projects and leading banks to take advantage of location, encryption, digital signatures, secure remote access, etc. The development of public and private cloud computing platforms has made working with financial data too easy and easier.
Artificial intelligence and financial technology: happily married?
AI has brought a new stage in the development of applications and services in the financial market. As is known, AI is able to process unstructured data such as images, presentations, video, audio, location and time series perfectly. That is why the existing AI-based solutions offer many options: With their help, the fraud can be identified, the creditworthiness and risks assessed and a person identified based on their digital footprints. In the insurance area, they are used to identify insurance fraud, automate claims and improve risk management.
The AI-controlled chatbots , which have been spoken a lot about in recent years, help the user experience to be personalized in real time and in the most efficient way. This enables banks to reach the next level of maturity in their customer relationships and experiences.
Finally, virtual assistants should be mentioned. This is another AI product category that is very popular with banks and financial companies, and just like bots, helps guide the user through the bank’s services and products, thereby enhancing the user’s journey, providing insights and targeted actions to increase target conversation.
Let’s take a quick look at some of the most exciting AI initiatives launched by banks and financial institutions.
Effective use of banks’ AI technologies
JP Morgan uses AI to automate loan contract analysis. JP Morgan recently implemented a new program called COIN, which stands for Contract Intelligence. This platform allows users to analyze contracts, highlight key terms and critical data. So far, bank employees have spent a total of 360,000 hours a year doing these mundane tasks.
Wells Fargo announced the creation of a dedicated AI team to develop innovative payment technologies and improve services for its corporate customers. A special role of the Wells Fargo AI team is to develop the technologies that should enable the bank to provide more personalized online customer service.
Current projects the AI team works on behalf of the bank include systems that can detect payment fraud or misconduct by employees, as well as technologies that can give customers more personal recommendations on various financial products.
Bank of America developed an AI-based virtual financial assistant called Erica. This is a chatbot that is already available to the bank’s 25 million mobile customers free of charge in the BofA app. Erica is AI-driven and combines predictive analytics and natural language to make it easier for BofA customers to access account balance information, transfer funds between accounts, send money with cells, and schedule meetings in financial centers. Customers can interact with Erica in any way, including voice commands, text messages, or tapping options on their phone’s screen.
CityBank places particular emphasis on developing and investing in AI-based startups and projects that are designed to use AI to detect and combat fraud in online banking. For example, it has invested in a data science company Feedzai that uses real-time machine learning to identify fraudulent payment transactions based on big data analysis and to minimize risk in the financial industry. Another example is a personal finance app, Clarity Money, which combines machine learning with AI to create a product that customers can use to manage their finances. This app accompanies customers on their financial journeys and helps them make smarter decisions regarding budgeting, money management and spending.
According to a press release, “Feedzai’s machine learning method automatically adjusts controls to monitor deviations and changes in customer payment behavior. This enables the analysis and identification of potential anomalies in the affected payments even before they are sent for processing. This will be done while ensuring fast and efficient payment processing. ” CitiBank expects its innovative solution to be launched this year.
How is AI used by FinTech startups?
The financial services industry is very popular with startups. While some efforts from startups to make a revolution in traditional banking, others try , to help banks process of expanding its services with new and advanced products and improve. The AI use cases from a FinTech startup world include, for example, fraud detection and advisory services, personal financial management, transaction support and so on.
When comparing consumer behavior with numerous historical data, the smallest details can be found and cyber fraud can be prevented in advance. AI tools collect data and receive updates, which is why they are continuously trained and improved.
The AI-based advisory robots can reduce risks for customers because they are able to recommend suitable financial products and objects for investments via a large amount of information sources.
A particularly promising area for FinTech startups is personal financial management. The successful startups here are online budget planner Mint and personal finance manager Wallet.
These platforms perform the following tasks: collecting information about personal finances, managing data over time, and making informed decisions and recommendations. One of their advantages is ease of use. They are also popular with those consumers who previously had insufficient patience to control their finances.
Some of the most promising AI startups in finance
DreamQuark develops a software platform that democratizes the use of artificial intelligence and is used to develop and design AI applications specifically for the banking and insurance sectors. The solution covers all of its main activities with dedicated applications such as customer segmentation, targeting, underwriting, credit assessment, asset management, anti-money laundering, fraud, dunning, satisfaction and customer loyalty.
Alpaka combines human resources and AI to develop a new collaboration platform for global capital markets and offers unique AI-based market forecasting solutions for global financial institutions. A detailed high-frequency data training (machine learning) is used for their market forecast models, whereby typical scenarios that indicate the price changes are recognized. Alpaka offers MarketStore for fast, scalable data storage. This is an open source database server that is extensively optimized for financial time series data.
DataVisor offers the world’s most advanced AI-based solutions to detect fraud and other financial crimes and protect companies from fraud and abuse. The company uses unsupervised machine learning to detect and prevent modern, sophisticated cyber attacks. As a result, the performance of companies using DataVisor products is 50% more efficient than that of their competitors.
Quantexa is a big data analytics company that provides actionable information in the fight against financial crime and customer intelligence. It uses the latest developments in big data and AI to predict default risks, proactively detect fraud, prevent money laundering, profile unscrupulous players and trusted customers, and describe the connections between them.
Thanks to FinTech, banks have learned to be user-centric and anticipate future needs
Compared to Tesla, which is perceived more than just a vehicle these days, banking services are also becoming entire ecosystems. As a user, we are fortunate that someone is currently developing a new robotic advisor who will tell you where to invest your money and who will use your father’s voice to make the recommendation as personal as possible. In this way, artificial intelligence helps banks and FinTech startups gain a competitive advantage and make a difference in terms of usability.
If you have any questions or suggestions on how AI banks and startups can help, you can contact the Tech By light team by email or Q&A .