The financial industry was one of the first to adopt and enjoy the benefits of artificial intelligence (AI). The annual budgets of large banks amount to billions of dollars, which is comparable to the state budgets of some developing countries. No surprise that banks and financial organizations will be the key drivers of AI R&D in FinTech. They’ll also be the ones to bridge the AI knowledge gaps across other industries and support FinTech startups ecosystem.
The largest and most successful credit organizations have already developed official AI strategies.
Most of the strategies imply that internal or outsourced AI departments and teams be launched. According to the forecast of Autonomous Research, by 2030, AI technologies will allow banks to reduce operating costs by 22%. Savings of financial institutions can reach $1 trillion in the long run.
At the same time, an acute problem for large banks is the lack of qualified AI developers and data analytics specialists. The lack of devs can slow down the development of technology in many sectors, with FinTech being damaged the most.
The previous wave of FinTech startups and client applications in the field of financial services was associated with the proliferation of smartphones. At about the same time, the term “FinTech” itself appeared. Smartphones allowed FinTech projects and leading banks to take advantage of client location determination, encryption, digital signature, secure remote access, etc. As public and private cloud computing platforms emerged, work with financial data became streamlined and facilitated.
AI and FinTech: a Happy Marriage?
AI gave birth to a new wave of applications and services in the financial market. Since AI can handle unstructured information such as images, video, audio, location, and time series data perfectly well, there are already AI-based solutions able to detect fraud, assess creditworthiness and risks, identify a person based on his/her digital footprints, etc. In the insurance sector, they help identify insurance fraud, automate claims cases, and improve risk management.
By using AI-driven chatbots, banks can take their customer relationships and experiences to the next maturity level, as they help personalize UX in real-time and in the most efficient way ever.
Another AI product category that’s extremely popular among banks, and financial companies is a virtual assistant. Just like bots, they help walk the user through the bank’s services and products and, thus, improve the user journey, provide insights and set specific calls to action to increase goal conversions.
Let’s take a look at some of the most exciting AI initiatives launched by banks and financial organizations.
How Banks Leverage AI Technologies
JP Morgan: uses AI to automate the analysis of loan agreements. The bank recently introduced the Contract Intelligence platform (COiN) which allows users to analyze such agreements, highlight the key terms and conditions, as well as critical data. Previously, this work required 360 thousand man-hours to complete.
Wells Fargo: announced the creation of a special AI team that will be engaged in developing innovative payment technologies and improving services for its corporate clients. In particular, Wells Fargo’s AI team will work on creating technology that can help the bank provide more personalized online customer service.
Current projects assigned to the bank’s AI team range from systems that can spot payments fraud or misconduct by employees to technology that can make more personal recommendations on financial products to clients.
Bank of America launched Erica, an AI-based virtual assistant that is planned to be integrated into a mobile app and several ATMs all over the country.
CityBank: has recently invested in several AI-based startups and projects like Feedzai that uses AI to detect and fight fraud in online banking. Another example is a company called Clarity Money that leverages the power of AI to help clients choose financial products and manage their assets.
According to a press release, “Feedzai’s machine learning technology will automatically adjust controls to monitor discrepancies and changes in client payment behavior, allowing for the analysis and identification of potential anomalies in affected payments before they are sent for clearing. It will do this while ensuring that payments are processed quickly and efficiently.” Citi expects to launch its innovative solution in 2019.
How FinTech Startups Leverage AI
The financial services industry is desirable for startups. Some of them strive to make a revolution in traditional banking, while others tend to help banks improve their products with new and advanced services. There are several AI use cases from a FinTech startup world: from fraud detection and consulting services to personal finance management to transaction assistance, and so on.
Comparing consumer behavior with a vast array of historical data, we can find the smallest details and prevent cyber fraud in advance. AI tools are continually being trained and improved as they accumulate data and get upgrades.
AI-based consulting robots can help reduce risks for clients by recommending suitable financial products and objects for investment through a variety of information sources.
Particularly promising for FinTech startups is the sphere of personal finance management. There are already several successful startups here such as Mint and Wallet.
These platforms can collect information about personal finances, track data over time and make informed decisions and recommendations. They are convenient to use and will suit even those who previously could not have the patience to monitor their expenses and income.
The Most Promising AI Startups in the Financial Sector
DreamQuark: is a platform for developing and designing AI applications specifically for the banking and insurance industries. It can be used for product selection, customer segmentation, fraud detection, credit scoring, and credit check.
Alpaca: helps make predictions of events in the financial market. For their market forecasting models, they use in-depth high-frequency data training (machine learning, or ML) to recognize typical scenarios that indicate price changes. They developed MarketStore — a highly scalable, proprietary database server optimized for working with time series of financial data. Now, this software is entirely open-source.
DataVisor: uses AI to detect fraud and other financial crimes. The company applies unsupervised ML models to find previously unknown fraudulent schemes. As a result, companies that use DataVisor products report their performance to be 50% more efficient than that of competitors.
Quantexa: is another exciting new FinTech startup. It uses AI to predict risks of default, proactively detect fraud and create profiles of both unscrupulous actors and trustworthy clients, as well as describe the links between them.
FinTech has taught banks to be user-centric and to anticipate future needs.
Just as Tesla is more than just a vehicle, so are banking services: they become entire ecosystems. As users, we are fortunate: at this very second, someone is creating a new smart robot consultant that will tell you who to invest in and will use your dad’s voice to make the recommendation as personalized as possible. That’s how AI helps banks and FinTech startups gain a competitive advantage and make a difference when it comes to user experience.