The rapid pace of digitization has caused fintech markets to boom around the world. The market for Fintech was over $112 billion last year, but is projected to be worth over $333 billion by 2028.
During this wave of disruption, successful business owners and startup founders need to understand the technologies that are driving the industry forward. Artificial intelligence is one of the most important trends pushing the envelope of what’s possible with fintech. Let’s talk about AI’s benefits in fintech, especially in terms of app development and how you can use it to maximize the success of your business.
When Fintech Meets Artificial Intelligence
AI can benefit financial technology in a number of ways. It can result in more active user engagement, secure payments, and efficient workflows. From these benefits ultimately come cost savings. Since the tasks AI can perform reduce time significantly, teams can invest more in other aspects of a project. So it is not surprising that AI use in the fintech market is growing. Mordor Intelligence predicts that the technology’s value in the industry will grow at a CAGR of 23.17% until 2025.
Fintech app developers are adopting AI in their applications to solve a number of business challenges, such as providing a high level of customer support. Since AI can automate customer service to a degree with chatbots, AI assistants and other methods, teams can spend less time on repetitive questions and focus on more complex cases.
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Artificial intelligence is also adept at data processing and analytics, both useful tools for financial applications. It can be leveraged even more effectively with Agile technology. Interpreting data to generate valuable insights on analytics for consumers can help users improve their financial habits and achieve their financial goals easily. It can also help businesses plan for the future and make wiser investing choices. That’s why artificial intelligence is widely used in budgeting apps, financial assistant apps and investment platforms. For example, the world’s popular money management apps such as Cleo and Fyle have AI at their core and leverage the power of this technology to provide intelligent financial tools. This gives them competitive advantages and opens additional business opportunities.
Top Use Cases of AI in Fintech
Let’s review some use cases to get a deeper insight into how AI can empower the fintech app development process.
OCR for Processing Receipts and Invoices
Document digitization is one of the most time-consuming tasks that finance teams face. In addition to this, often the concept of a fintech application includes fast and automatic recognition for receipts, invoices, and other financial documents to enable accurate spending tracking, KYC/AML processes, reporting, and so on.
Based on Optical Character Recognition (OCR), apps like Extracta, and Klippa solve these challenges by converting financial documents into accessible text in minutes. All the user has to do is take a photo of the invoice with their phone and run it through the program. Simplifying the process like this makes it easier to keep track of costs and reduces the manual effort required to do so. This can also reduce the cost of data entry for businesses, as this process becomes automated with OCR.
Using OCR for KYC processes allows you to automate the verification of documents such as an identity card, driver’s license or passport. The AI should be able to determine the required data like name, photo, address, and contact details and extract that info for further validation. All this is done in a matter of minutes and significantly speeds up the identity verification process.
Natural Language Processing for Speech Recognition and Voice Assistants
One of the many challenges fintech has had to overcome in the past few years is how to automate financial assistance. With the rise of consumer assistants like Siri, Amazon Alexa, and Google Assistant, it seems possible that AI can provide financial assistance and advice as well. Many banks have already begun to utilize chatbots powered by natural language processing, also known as NLP. This technology leverages AI to communicate with humans more seamlessly.
NLP chatbots can automate the workflow and collect valuable data through these interactions. They are also sensitive to details like mood and satisfaction. With NLP, AI can also search through documents more effectively and present them to users in a streamlined form. By taking complex documents and simplifying them into a more digestible format, AI can help users understand how to improve their financial behaviors.
AI Biometrics for Authentication
Keeping financial data secure is essential to prevent fraud. That’s why AI biometrics in fintech applications are becoming more common. It’s a misconception that biometrics are impossible to trick. Many hackers aren’t using complicated console commands to hack into your system. Instead, they’re using printed-out selfies to fool facial recognition and fake fingerprints. While traditional biometrics may be fooled by these spoofing methods, artificial intelligence is more resilient. AI can detect unusual patterns in behavior to prevent threats.
Predictive Analytics
Artificial intelligence works best when paired with real-time data. With financial technology apps, predictive analytics has a number of benefits. For example, users can get forecasts on their income or expenses in the future. This is a useful feature included in many budgeting and financial assistant apps.
Predictive analytics is helpful not just for consumers. In fact, there are more profound applications of predictive analytics for fintech businesses. Being able to forecast demand and revenue, improving cash flow management, and predicting financial risks are all ways that fintech companies can remain relevant in the modern market.
Key Challenges of AI in Fintech
As exciting as the benefits of artificial intelligence for fintech app development are, to be successful, you need to understand all the challenges of implementing AI in fintech. Partnering with expert fintech developers will help you get around these complexities and create a quality AI-powered fintech app.
Bias
Many assume that artificial intelligence is the most objective way of completing tasks. However, this isn’t true. Although AI may act autonomously, it relies on our instructions. That data is provided by humans and is subject to bias. Good engineers know how to mitigate this bias. The first basic step is to compare and test different samples of training data for representativeness.
Compliance and Privacy
Regulations are another important challenge. Since AI relies on high-quality and high quantity data, staying compliant with data collection laws is crucial. A few laws that firms will need to pay close attention to are SOC2 Type II, HIPAA, GDPR, and CCPA. The full list of regulatory frameworks will depend on your region and what kind of data your application will handle.
Success Metrics
Metrics of success for one fintech firm may be different than another. Users may not check the app every day and instead have different behaviors, such as once a month or during an important financial change. Just because they aren’t using the app as often doesn’t mean the app has failed. Seeing if the app has helped change their behavior while offline may be even more important. The user should feel more in control of their finances as a result of using the application.
Wrapping Up
The development of fintech is closely related to the adoption of AI technology. Artificial intelligence helps create smarter applications that can improve the efficiency of financial management and compete with traditional financial agents.
However, implementing AI in fintech requires deep technology expertise, so don’t hesitate to enlist the support of experienced fintech developers. With the right team, you can leverage the power of AI to maintain your fintech application’s relevance for the foreseeable future.