AI and Machine Learning for Fintech: Benefits, Challenges, and Use Cases
While artificial intelligence is something alien for many industries, the market for AI in fintech is expected to grow at a 23.17 percent compound annual growth rate (CAGR) from 2020 to 2025 (Mordor Intelligence).
Also, let’s not forget that the COVID-19 pandemiс has had the strongest effect on fintech and its customers, who are now using mobile apps more actively. This is a golden opportunity for businesses to get more meaningful insights on users, improve their experience, and, as a result, strengthen their market position.
Here you’ll find the latest market trends, use cases, benefits, and challenges of AI in fintech based on our insights and expertise.
AI in Fintech: The Main Benefits for the Industry
Before we start, here’s some statistical data on the main incentives for fintech firms to use artificial intelligence according to the Cambridge Centre for Alternative Finance. Spoiler alert: the customer is still king.
1. Higher user engagement. AI solutions keep an eye on app users, answer their questions immediately (chatbots), or gather analytics on user preferences and behavior patterns.
2. Optimized workload. AI solutions assist fintech employees with routine tasks such as answering typical questions, classifying clients, and monitoring transactions and emerging regulations.
3. Reduced user support cost. AI and machine learning in fintech eliminate the risk of human error and save on your user support spending over the long haul.
4. Secure payments. Fintech and AI together ensure constant payment monitoring and user verification, covering numerous security gaps invisible to humans.
5. Data-driven decision-making. AI peers into every shadowy corner to help you: it collects documentation, forms reports, and makes predictions. You get a forceful tool to build actionable business strategies.
6. Attention to detail. With AI, you will always be aware of what’s going on in your organization. What can be overlooked by managers will never pass undetected by tech tools — this rule applies to any data management task.
How Is AI Used in Fintech? The Top 6 Use Cases
How exactly do AI and ML work, and what use cases do they have in fintech? You’ll find it all here.
1. Chatbots as digital financial advisors
The core of this use case is Natural Language Processing (NLP), one of the machine learning fields. It enables technologies to better understand and analyze natural (human) language. Such a feature allows users to “consult” a chatbot regarding financial services, plans, spending, and deposits.
2. Credit history assessment
Often, people in developing countries don’t have a previous credit history, and banks have to approach it using NLP and text mining. This combination of tools fetches data from the client’s digital footprint (like browsing history or social media presence) and builds up a credit history with no human involvement.
3. Risk score profiling
Using Artificial Neural Networks (ANNs), developers can train technologies on the user’s historical data and then classify their profile from low to high risk level. Above that, technologies also can provide clients with service recommendations based on their risk score.
4. Financial trends prediction
ML-based predictive analytics gathers and processes vast amounts of data to make insightful predictions about market trends and client behavior. It processes not only structured data sets but also identifies changes in common patterns.
RegTech (Regulatory Technology) is a way to manage regulatory compliance with the help of AI algorithms. It is a complex term that includes client identification, transaction monitoring, regulatory analysis, and reporting.
6. Algorithmic trading
Algorithmic trading is an ML-based trading method that assists decision-making in the financial market. Technologies spot patterns that can pass unnoticed by human eyes; they react instantly and manage trading automatically.
Challenges of Integrating Artificial Intelligence in Fintech
Of course, it’s not all sunshine and roses — combining artificial intelligence and fintech imposes challenges as well.
1. Poor data quality
Remember that fintech and machine learning work the best when documentation follows a certain order. It should be stored in spreadsheets or databases for the maximum positive impact, so getting into unstructured data management right now is the best way to overcome this challenge.
2. AI bias
To avoid this issue, companies should detect bias before they do any harm, using special AI frameworks and toolkits and providing policies.
Examples of instruments you need to remove AI bias:
3. Need for human-to-human interaction
No matter how cool ML and AI are in coping with business tasks, they can’t replace heartwarming human interaction. You should decide what you need chatbots for, and accurately delineate AI and human tasks. Only a smart combination of both will have the biggest influence on the company.
We’ve also prepared the latest market trends, questions to ask yourself before connecting AI and fintech, and things to consider for AI deployment. All that and even more are gathered into one guide. Click here and enjoy!