Over the past few years, the financial world has been increasingly adopting smart solutions to cope with the industry’s changing landscape. Artificial intelligence (AI) and machine learning (ML) have almost thoroughly infiltrated almost every area imaginable,
from back-end processes to forward-facing front-end roles.
In 2019, the use of AI in Fintech alone reached an estimated value of $6.67 billion. This is expected to grow to over $22.6 billion in just five years. And with a compound annual growth rate (CAGR) of 23.37 percent, there are no signs of any slow-down. But
why do we see this growth, and why now?
What’s behind the drive for AI and ML?
It’s not just the fintech world. Across almost all industries, we see a higher demand for AI and ML solutions. The world itself is developing at a never-before-seen pace.
Currently, AI technology holds a global market value of $10.1 billion and is used to solve various business challenges. Some of the most frequent use cases of AI and ML include:
- 38 percent – Reducing costs
- 37 percent – Customer insights
- 34 percent – Customer experience
- 30 percent – Internal process automation
- 27 percent – Fraud detection
- 26 percent – Customer satisfaction
Simply put, these technologies are forces for change across almost all business areas. They provide a deeper understanding of customers’ needs (Big Data) and company processes, empowering businesses to optimize and refine their market offerings for a changing
world. Fintech is no exception.
What key areas are financial service providers targeting in 2021?
In 2021, we are set to see a continuation of on-demand in the financial industry. What this means is customers are expecting services to be delivered faster and more personalized than ever before. These are the trends to watch out for in the coming year.
In keeping with the finance on-demand theme, personalized portfolio management and product recommendations are two of the most-sought-after AI/ML solutions for this year. Although debates abound related to such technologies’ accuracy and ethics, their popularity
is growing, as is their refinement. The latest solutions can recommend clients’ investment opportunities based on their income, current investment habits, risk appetite, and more.
Robo-advisors have seen advancements from online questionnaires to dedicated fund and portfolio management to algorithm-based rebalancing and proposals. As we head into 2021, we are set to observe a refinement of systems and more fully-automated, self-learning
algorithms to aid investors.
Currently, one of the most commonly applied uses of AI and ML in fintech, process optimization, will only continue to rise in 2021. Process optimization aids companies in reducing the amount of manual work done by employees and, in general, makes the process
more efficient, increasing productivity. Often it is used to automate call-center functions, optimize paperwork for customer-facing chatbots (more on this later), and improve employee training.
In the coming year, we are likely to see a refinement in these technologies and a push towards automating more systems, such as replying to customer queries, report generation, Big Data analytics, which will give more significant insights into the business.
Current credit scoring systems are outdated. They base decisions on supposed demographic profiles, including occupation, age, race, gender, etc., and take little account of the person making the application. AI and ML empower companies to profile customer
risk more accurately, taking into account the person, not a stereotype.
Engaging credit scoring technology can reduce non-performing loans up to 50 percent while boosting return up to 30 percent, meaning better loan decision-making. Such technology works by building models, validating them to check their work, and then deploying
them to the market quickly. This means companies are less likely to lend to risky clients, and customers can access services faster, getting an answer to their credit decision when they need it. The future is personalized, almost instant loan decision-making.
According to Experian, over 55 percent of businesses worldwide reported fraud in the last year, with 3 out of 5 saying this has increased in the previous year. Some of the top concerns are related to account opening and take-over fraud. However, fraud in
the financial industry is nothing new. As long as there has been money, there have been those that are willing to commit fraudulent acts to get it. The switch to digital has only meant fraudsters need to be more creative in their actions. That’s why it’s vital
that providers stay one step ahead.
With 88 percent of customers stating appearances are everything when it comes to trusting a financial provider, organizations need to stay up to date on the latest security techniques and let their customers know about it. The next year will see an increase
in AI and ML security solutions. For example, analyzing documents for account registration (RegTech), detecting anomalies in patterns within accounts, and more.
There’s no doubt, your customers’ opinions matter. 93 percent of consumers are more likely to come back to a business with excellent customer service. But what is great customer service? In today’s world, it boils down to two things – response time and personalization.
This is where AI and ML come into play.
With 90 percent of customers wanting an immediate response to their questions, those precious minutes and seconds they don’t receive a response allow another company to take a competitive advantage. AI and ML chatbots, however, are grabbing those moments
back. They engage customers quicker than ever before and are smarter too. The technology not only allows them to answer customers’ inquiries but to gain insight into customer needs.
The more data that is analyzed means, the more a customer experience can be tailored. This can be anything from answering a question to delivering a personalized product to them, for example, a loan to suit their income and risk, or information before progressing
in a transaction. The power is endless.
Are there any drawbacks to adapting the technology?
Every innovation has its critics, and while AI and ML solutions are advancing rapidly, they are not flawless. If you’ve ever had your bank question a transaction you made that it had deemed fraudulent, you’ll know this. False-positive and buggy systems exist.
However, that said, they are being ironed out. As technology advances, AI and ML will become smarter and more adapted to human behavior, allowing them to deliver more accurate results.
How should a business adopt AI or ML solutions for maximum effectiveness?
Choosing how or where to adopt a new solution is no easy feat for any business. Although 84 percent of C-level executives believe in the need for AI in their companies to meet growth objectives, 76 percent don’t quite know how to get there. Scaling using
AI and ML isn’t easy, but it is essential for any company looking to progress.
Our advice for getting started is to choose a single element to work from. Whether this is customization for a personalized experience, smarter internal decision-making, foresight into the business trends, interaction with clients, or pattern detection for
fraud targeting, or something else, it’s essential to focus on business needs and find the AI/ML solutions that work for your business.
By Dmitry Dolgorukov
A Co-Founder and a CRO of HES FinTech and CEO at GiniMachine – an AI-driven platform
that solves traditional credit scoring challenges with machine learning algorithms.
– In 2018, he was ranked as one of the top 200 Fintech leaders in Europe who contribute to
the industry as influencers through action.
– In 2019, Dmitry was ranked as one of the most influential AI leaders in Eastern Europe.
– Since 2020 Dmitry Dolgorukov is a member of the Forbes Finance Council.