8 Best Use Cases of Artificial Intelligence In Fintech with Examples

AI in fintech is a powerful catalyst, seamlessly integrating into financial operations to enhance security, streamline data processing, and deliver personalized customer experiences. According to a McKinsey global survey, about 60% of companies use AI in at least one business function (source ).

In this article, you will explore examples of how AI is being leveraged to transform the financial industry through increased security, behavior-based investment predictions, automated data processing, human-like interaction with customers, financial reporting, and fraud detection. Keep reading and learn how AI will help fintech companies and how AI is already changing the fintech industry.

Source: KPMG

8 Use cases of AI in fintech with examples

1. Financial reporting

Fintech and AI found each other largely due to the ability to create financial reports. They may be generated using AI and financial data. Banks and other financial institutions contain vast amounts of data, which they use to generate reports after careful research. After thoroughly reviewing the data, these reports must be produced, which takes time. Companies frequently require these reports, notwithstanding the possibility that the input data sets may vary.

Wealthfront

With 440,000 members and $25 billion in assets under management, Wealthfront is one of the top robot advisors in the market. The minimum account requirement is only $500, offering many investing possibilities, including cryptocurrency. It also has relatively cheap costs, with most accounts paying just 0.25% and no transaction fees. Since UBS bought Wealthfront at the beginning of 2022, the company is anticipated to grow even more quickly.

2. Customer experience

There are hundreds, if not millions, of customers at huge financial organizations. Catering to so many different individuals without AI-powered technology would be a huge undertaking. Personalization may boost brand loyalty and customer confidence in your business.

When clients download banking apps, customer data is gathered and analyzed by AI systems. Information can be used to provide pertinent, pre-approved products and specialized financial advice. In banking apps, AI can help users track their spending goals and financial objectives.

Due to their inherent learning ability, AI systems will only get better at reading client data and providing individualized experiences. Artificial intelligence (AI) models assess voice and speech traits to produce useful information and can separate precise patterns from monotonous babbling. Analysis of AI transcripts can predict a customer's mood and direct agents as to what solutions to offer them.

Kasisto

KAI, a conversational AI platform used in the banking sector to enhance client experiences, was developed by Kasisto. By providing customers with self-service alternatives and solutions, KAI helps banks lower the traffic of contact centers. Additionally, AI-powered chatbots help customers make thoughtful financial decisions by offering sage advice.

3. Fraud detection

Artificial intelligence can respond instantly to the data supplied to them, recognize patterns and correlations, and maybe even spot fraudulent activities. This is extremely helpful for the banking sector, as there is a staggering amount of digital transactions per hour, necessitating greater cyber security and effective fraud detection. Fraud analysts no longer have to do as much work, freeing them to concentrate on higher-level problems, while artificial intelligence works in the background to find the lesser ones.

Vectra

Vectra developed Cognito, a technology for identifying and pursuing cyber threats. The software from Vectra automates threat detection, finds covert attackers, particularly those who target financial institutions, quickens event investigations, and even finds compromised data.

4. Data analytics

The stock market has become one of the finest investment options for millennials. This trend has generated a big demand for AI-driven apps that help people navigate the stock market more effectively.

Several smartphone apps with AI backing now examine historical and current data about businesses and their stocks. Additionally, they assist investors in determining which stocks are suitable for investment and which would be a bad choice.

Perfios

An effective data analytics platform is provided by this Indian business, mostly employed by banks and non-bank financial institutions (NBFCs). It aids in fraud prevention, better loan selections, asset management, and obtaining trustworthy credit scores. Deutsche Bank, Canara HSBC, and Home Credit Finance are just a few companies that Perfios has as clients and has received over $120 million in investment.

5. Algorithmic trading

Algorithmic trading is one of the most popular applications of AI in fintech and a cornerstone of modern financial markets. AI-driven algorithms analyze vast datasets at lightning speed, identify market trends, and execute trades with split-second timing.

This automated approach mitigates the risk associated with human emotions in trading, ensuring that financial decisions are based on data-driven insights, maximizing returns, and minimizing losses.

HDFC Bank

Renaissance Technologies LLC, a hedge fund based in New York, is one of the world’s most successful algorithmic trading firms and AI use cases in fintech. The firm’s Medallion Fund has generated average annual returns of 66% since its inception in 1988. The fund uses a range of quantitative trading strategies based on mathematical models and data analysis (source ).

6. Digital banking

Traditional banks, often called incumbent or established financial institutions, face stiff competition from a new wave of players known as neobanks or challenger banks. The challengers vying for their throne put pressure on established financial institutions.

Digital banking breaks down geographical barriers and provides 24/7 access to financial services, making banking more convenient for customers regardless of their location. Mobile apps and online platforms enable account management, payments, and transactions from the comfort of one's smartphone or computer.

Also, because of automation and the absence of physical departments, digital banking significantly reduces operational costs. This leads to lower fees and better interest rates for customers, making financial services more affordable.

If you can't implement AI for fintech advantage, you must find other ways. For example, established financial institutions have responded by digitizing their services, partnering with fintech firms, or launching digital banking platforms to remain competitive.

Monobank

Monobank is currently the fastest-growing bank in Ukraine. It operates exclusively through its mobile app, offering a seamless and accessible banking experience.

The app facilitates various services, such as free money transfers, no-commission utility bill payments, multi-currency checking and deposit accounts, credits, and appealing cash-back incentives. Customer support is easily accessible through messengers or by phone.

Monobank employs advanced technologies, like neural networks for image recognition and gradient boosting for credit risk assessment, analyzing over 2000 customer data parameters. Monobank's impressive user acquisition and market impact highlight the transformative power of digital banking and the use of AI in fintech in Ukraine and beyond.

7. RegTech

RegTech, short for Regulatory Technology, offers a compelling solution to the challenges of keeping pace with regulations and document preparation, which are often time-consuming and prone to human errors.

Automated compliance software becomes a logical choice in this context. Examples of RegTech encompass chatbots capable of offering regulatory guidance, cloud-based platforms designed to manage regulatory and compliance data efficiently, and innovative computer code facilitating streamlined data processing related to regulations.

Dash Regulatory Technologies

Hummingbird proudly identifies itself as a leading RegTech solution, offering a dedicated CRM platform meticulously crafted for compliance and risk teams. It specializes in providing financial institutions, including banks, fintech companies using AI, lending institutions, and credit firms, with a robust anti-money laundering (AML) system.

Hummingbird's primary mission revolves around leveraging RegTech capabilities to significantly bolster AML efforts, enabling the proactive identification and prevention of financial crimes on a broader and more effective scale.

8. Robo-advisors

When humans and robots work together, everyone wins. Humans may concentrate on more strategically and creatively oriented activities, while robots automate and simplify back-end office tasks. Robo-advisors is a great example of AI-based fintech solutions.

Wealthfront

It is a California-based automated investment company. It is a Robo-advisor offering assistance in planning one’s goals, transparency in building one’s portfolio, and various account services. Furthermore, it has reported assets under management of around $20 billion as of September 2019 (source ).

What financial niches and services can benefit from AI

The role of AI in fintech can't be overestimated. It can be applied across various financial institutions, including but not limited to:

• Banks: Both traditional and digital banks can leverage AI for customer service, fraud detection, credit scoring, and chatbots.
• Insurance companies: Insurers use AI for claims processing, risk assessment, underwriting, and personalized customer service.
• Asset management firms: Asset managers employ AI for portfolio optimization, market analysis, and algorithmic trading.
• Hedge funds: Hedge funds use AI for quantitative analysis, trading strategies, and risk management.
• Payment processors: Companies in the payment industry use AI for fraud prevention, transaction monitoring, and improving payment processing efficiency.
• Credit unions: Credit unions can use AI for risk assessment, member services, and loan approval processes.
• Venture capital firms: Venture capital firms utilize artificial intelligence for fintech for investment analysis, due diligence, and identifying promising startups.
  

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3 Benefits of using AI in fintech

3 Benefits of using AI in fintech

The benefits of AI in fintech are numerous, and it's no wonder that many financial institutions are turning to this technology. Let's take a look at 3 key advantages.

Cost-saving

Based on McKinsey's report, 44% of businesses adopt AI technology to lower company costs in areas (source ).

The fintech sector can save billions of dollars in resources, labor costs, and capital using AI-powered solutions. Given the labor cost, manual processes frequently take longer and cost more money. By replacing these tasks and activities, AI automation saves resources. As AI technology answers most questions, customer service teams spend less on hiring new employees.

By providing tailored insights, preventing money laundering, and conducting credit underwriting in the back office, AI helps banks save money in all three areas of their operations.

Due to the cost savings from implementing AI technologies, banks can offer better deals and draw in more clients. Saving money while increasing revenue and profit as the result.

Improved customer experience

According to recent statistics on artificial intelligence, 62% of consumers use AI to enhance user experience (source ).

With artificial intelligence already making considerable strides in customer support for banks and fintech businesses, customers are growing accustomed to receiving prompt replies at any time of day. To facilitate transactions and answer questions, financial institutions must be accessible around-the-clock, every day of the week.

Call centers are regularly under pressure to clear backlogs while offering assistance continuously. Chatbots, virtual assistants, and other AI-powered interfaces reduce workload by addressing common user queries and issues. This gives customer service representatives more time to handle complicated inquiries.

Advanced sentiment analysis, which focuses on assessing the client's experience, identifying gaps, and training chatbots to close those gaps, is one way AI is assisting in improving fintech customer service. AI-based solutions make communicating with the finance industry simpler and more convenient for clients. More contented clients and customer service staff translate into a more successful business.

Better analytics

Around 48% of companies use AI in fintech to address data quality challenges and enhance analytics, based on the O'Reilly report.

Systems with artificial intelligence (AI) can analyze data from hundreds of sources and predict what will work and what won't. AI can also conduct in-depth customer data analyses and make predictions about consumer preferences, product development, and distribution methods.

Human analysts cannot compete with the effectiveness of well-trained algorithms. Unlike a person, an AI allows you to examine its inner workings and see precisely how a decision was made.

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AI technologies used in fintech today

AI-driven solutions not only enhance operational efficiency but also provide a more personalized and secure financial experience for customers. Here are some of them.

Speech recognition

Fintech companies employ speech recognition to power voice-activated virtual assistants that provide users with instant access to account information, and transaction histories, and even execute financial transactions through natural language commands.

This technology has not only simplified customer service but also bolstered security through voice biometrics, enabling secure and convenient user authentication. Moreover, it's instrumental in compliance and fraud detection, as it can analyze voice patterns to identify suspicious activities in real time.

Natural language processing (NLP)

It is a core AI technology increasingly used in fintech for sentiment analysis. NLP algorithms can analyze vast amounts of textual data, including news articles, social media posts, and customer feedback, to gauge market sentiment and make informed investment decisions. Fintech firms use NLP-powered tools to track news and social media around financial assets, helping traders and investors react swiftly to market trends and news events.

Additionally, NLP-driven chatbots enhance customer service by understanding and responding to customer inquiries and complaints, providing a more personalized and efficient experience.

Computer vision

Fintech companies use computer vision to extract and verify information from documents like IDs, passports, and financial statements. This technology streamlines customer onboarding, reduces the risk of identity fraud, and ensures compliance with regulatory requirements.

Computer vision algorithms can accurately extract and analyze text, images, and even handwritten information from documents, improving the efficiency and accuracy of Know Your Customer (KYC) processes in the financial sector.

Binariks will help you with AI implementation

First off, the value we offer is the result of industry knowledge. At Binariks, developers have worked on projects for both large corporations and small businesses, bringing them outstanding returns on investment.

We make it easier to gather everything in one location. We design, engineer, and support fintech applications with AI. There is no need to oversee multiple contractors when one will do. The team you choose will be familiar with developing software that complies with domestic and foreign legal fintech standards.

We contribute technical and commercial knowledge to all projects thanks to our background in AI. This is disclosed to clients so they can quickly understand applied algorithms collaborating with us on your upcoming project using AI for fintech!

Our technique has been refined via hundreds of use cases in several sectors, and we have a track record of successfully tackling the key issues at each level. Therefore, we categorize them based on how well they perform throughout the experimental phase.

Final thoughts

Artificial intelligence offers the financial sector a special chance to save costs, enhance client satisfaction, and boost operational effectiveness, among other things. Financial institutions may provide their clients with top-notch financial services outside their branch offices.

Fintech firms worldwide employ various AI capabilities to increase the efficiency and safety of their operations. One key objective of all these solutions is to boost fintech organizations' productivity. Your staff will work much less hard if you use chatbots and automation tools for data analysis.

However, there is frequently still a need for more skilled AI programmers. Because of this, banks are turning to specialist software development firms with fintech-savvy engineers with years of expertise. Are you looking for an AI development team that works in the fintech industry? Contact us , and we'll assist you in maximizing these cutting-edge technologies.