Consumers of digital services expect quality service, security, and convenience. Machine learning (ML) in finance is the best example of how these needs can be fulfilled. Without compromising business models, and the need to generate revenue. With customer-centricity in mind. The best way to serve people what they need is to know them a little better. How is this goal achieved and what’s the state of machine learning adoption in the industry?
How can machine learning be used in finance?
To answer these questions, let’s take five and think about how exactly can ML be utilized. There are many uses of this technology. The most important factor is, however, the definition of machine learning that is adopted internally by us. As a software provider, we understand that there’s more to an application than mere code.
Machine learning in finance means algorithms on one side, and the right approach to software architecture on the other. The best way to utilize any advanced technology is to embed it in the business context and real-life, everyday use. Only then you can harness information, which is the primary fruit of using ML in the application.
5 best uses and outcomes of machine learning in finance projects
- Improved home budget management. Machine learning can and often is used to visualise data and flatten the playfield for everybody. Thanks to a design system and a clear display of complex data, people can understand their incomes, expenses and spending patterns. That leads to better money management.
- Enhanced customer support. FinTech companies frequently use AI-based assistance to help users. They can answer their questions, point to an article in the knowledge base, quickly and efficiently process claims. Between changing habits and the necessity to have one’s life in a pocket and pandemic social distancing, virtual assistants are invaluable.
- Improved loan processing and risk management. The more the application knows about the user’s habits, the more options it can provide for the customer. Machine learning eliminates human-based biases, therefore offering truly balanced loan products. Sure, artificial intelligence-based biases can also occur but companies are working hard to eliminate those. The other positive – deep knowledge about customers minimizes risks for loan providers. Cutting the provisions for all and making the product even more competitive, are welcomed side-effects.
- Increased personalization. There’s no FinTech without personal care. People left high street banks not only because of the 2008 financial and public relations crisis. They left because the whole new market was invented in its wake. Machine learning in FinTech provides insight into people’s lives, giving them personalized offers, and experiences. FinTech products are attractive because they can be tailored.
- Security and fraud detection management. The FinTech industry is highly regulated. It’s about money and its flows. There’s no room for error. Bad actors, people who want to criminally abuse the system, are on the constant lookout for security openings. They want to launder money and do other illegal activities. Machine learning in finance and accounting helps to prevent that.
The state of machine learning adoption in banking and finance
The process of learning is based on a huge amount of already occurred financial transactions. Algorithms must be fed data to analyze them and build credible patterns based on legitimate, real-life behaviors. The accuracy and usefulness of these patterns depend on the quality of the data. Looking at the market today, many companies have done their homework.
According to Deloitte’s 2020 report on digital maturity in banking services, the global market for artificial intelligence (AI) in FinTech will reach over $156 billion in 2025. With an annual growth rate of almost 24%, it’s clear what direction is important for sustainable growth development.
The report also predicts that the FinTech market will reach $309 billion by 2022. That’s a 25% growth rate a year.
54% of financial organizations that have over 5000 employees, adopted AI and ML solutions.
70% of all financial services companies utilize machine learning to detect and fight money laundering and other criminal activity. These solutions are also used to predict cash flow events and fine-tune credit scores.
That’s not the case for FinTech alone. O’Reilly’s survey shows that although software, finance, and banking industries are responsible for the highest machine learning adoption, healthcare, government, and even higher education, are to follow. The adoption of machine learning in finance projects is the most demanding, though.
Key challenges in machine learning adoption
The biggest ones are:
- Shortage of in-house domain knowledge
- Low quality of gathered data
- Poor condition and structure of the software, obstructing data mining
- Low accuracy of algorithms and models
- Budget limitations
Regulation is also very strict. For example, the UK’s Fraud Act of 2006, and 18 U.S. Code, Insurance Frauds Prevention Act in the US clearly state the responsibility. It lays in the hands of financial services providers. They are legally responsible for fraud damages, and that increases the cost of doing business. That’s why cost optimization is very important.
It generates future-proof solutions. According to Gartner, 40% of organizations point towards AI and machine learning as the biggest drivers to improve customer experience. Task automation is in second place, with 20%. Why? It’s about how machine learning can be used in finance.
User friction in FinTech, which is basically a set of pain points while using an app, has to be addressed. Using ML only to prove a point and be happy for utilizing modern solutions, doesn’t make sense. Companies have to make use of machine learning, to improve in-app quality of life. For the users and prospects, that look at app screens in smartphone-embedded shops. They instantly know if the layout is good for them or not. They install the app for functionalities but use a brief second to judge the overall usefulness. Creating advanced FinTech products isn’t easy.
Overtake machine learning – learn first!
In fact, FinTech product development is time-consuming and demands engagement from both – client and service provider. The outcome prevails over costs. If you choose the right partner, you get a product that is optimized, customer-centric, and legally compliant. Machine learning can do a lot for you, but you can be first when it comes to real wisdom. Choose wisely. After all, it’s your time, money and clients.
