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AI in Fintech. What we know so far in 2024

The growing AI in fintech market size and rapid pace of innovation point to how core technologies are revolutionizing financial services. 

26 April, 2024
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Customer service is a key opportunity for financial companies to better engage customers as interactions have traditionally been shorter on banking apps vs other industries. Rising customer expectations and cost pressures make it impossible for companies to rely solely on human agents.

Fintech companies are also facing pressure to automate routine processes in order to improve efficiency and reduce costs. As the volume of transactions and interactions grows, it is impossible for workers alone to manually process everything in time. 

This is where AI can help.

It can automate routine tasks to make operations more efficient. It can also learn from customer data to create personalized experiences. Together, these benefits have the potential to greatly improve customer service while reducing costs.

More people have adopted Fintech in recent years due to rising customer needs and advancing tech. To keep up and compete well in 2024 and beyond will require using AI solutions that can address different challenges across departments from one system. Let’s see what we know so far about AI in Fintech industry in 2024.

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AI in Fintech market: key findings

AI) is playing an increasingly important role within the Fintech industry. With technologies like machine learning, natural language processing, and big data analytics, Fintech companies are able to automate processes, gain valuable insights from data, and enhance the customer experience.

According to research from Dimensional Market Research, the global AI in Fintech market size is going to be valued at $17 billion in 2024. This rapid expansion highlights how Fintech firms are embracing AI to address evolving customer needs and stay competitive. McKinsey also estimates AI technologies could potentially deliver $1 trillion in additional value annually to global banking, with revamped customer service accounting for a significant portion.

Breaking down the market further, AI solutions such as robotic process automation, machine learning algorithms, and predictive analytics software accounted for 78.1% of the market in 2024. Their customizable, turnkey nature allows fintechs to efficiently tackle diverse challenges. When it comes to deployment, on-premise solutions led with a majority share due to benefits like enhanced security, regulatory compliance, and customization.

Within applications, business analytics and reporting are dominating with a 34.1% share in 2024. They empower informed decision-making, optimize processes, and ensure adherence to regulations. Lastly, North America accounted for the largest regional market share thanks to a vibrant fintech ecosystem, technological advancements among financial institutions, and strong integration of AI across operations and customer touchpoints.

Additionally, several key trends are shaping the AI in Fintech market:

  1. Technologies like hyper-personalization allow companies to provide customized services and products to each customer using their unique preferences and needs. 
  2. Due to regulations, explainable AI aims to ensure algorithms clearly explain their decisions to build trust. 
  3. Risk management utilizes AI to continuously examine vast amounts of transaction data to proactively identify and reduce risks. 
  4. Voice assistants and chatbots are creating more natural conversations between customers and their banks through apps and websites. 
  5. As more people conduct their banking on phones and computers, these digital-focused solutions powered by AI are becoming more popular among fintech companies trying to improve customer service in new ways.

Benefits of AI for Fintech

As AI continues to evolve, it will unlock new opportunities for fintech companies while also enhancing customer experiences. Here are some of the top ways AI is expected to benefit the thriving fintech ecosystem over the next year.

Improved customer experience

It is estimated that over 80% of fintech firms will soon integrate AI to enhance customer service. AI will power highly personalized chatbots and virtual assistants capable of addressing customer needs 24/7 through natural language interactions. 

Through analysis of individual financial behaviors and goals, AI will enable hyper-personalized recommendations, smarter budgeting tools, and personalized notifications. This type of experience will help companies retain customers and gain loyalty in an increasingly competitive fintech marketplace.

Increased operational efficiencies

Automating repetitive tasks and back-office work through AI leaves customer support agents free to handle more complex customer cases. 

One report found AI automation could reduce banks' operating costs by 22% by 2030. This cost savings allows fintech players to reinvest in innovation and scaling up operations. 

AI also facilitates faster transactions through automation, benefiting both businesses and consumers through a streamlined financial system.

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Stronger risk management

Leveraging AI, companies can more effectively monitor risks and identify fraud by spotting anomalies in vast datasets. One company reported an AI system can analyze risks 100x faster than humans alone. This helps businesses minimize losses from risks and strengthen financial stability over time.

Advanced analytics & insights

AI collects and analyzes massive customer data to provide actionable insights into spending behaviors, risks, and needs over time. This informs product improvements and hyper-personalization strategies. 

AI also optimizes trading strategies and delivers individualized investment recommendations for asset management firms and robo-advisors.

To sum it all up, AI will massively boost the growth and competitiveness of the booming fintech industry by transforming customer experiences, streamlining operations, enabling advanced analytics, and strengthening risk management - ultimately benefiting both businesses and consumers. 

AI technologies in Fintech

Machine learning is one of the most impactful AI technologies used in Fintech. By analyzing immense volumes of financial data, machine learning algorithms can identify complex patterns and trends to enable accurate financial forecasting and risk assessment. This helps financial institutions enhance services, proactively address potential issues for customers, and improve operational efficiency.

Natural language processing (NLP) is facilitating more intuitive customer interactions through chatbots and virtual assistants. Estimations say that over 50% of knowledge workers will use virtual assistants as their primary method of support. 

Beyond customer service, NLP also analyzes sentiment from social media and news sources to help Fintech companies make informed business decisions based on real-time public opinion data.

AI risk assessment and fraud detection systems analyze vast amounts of customer data using machine learning. They can detect anomalies and potential fraud risks that may elude humans. This proactive risk management approach protects customer assets and builds trust, ensuring security in financial transactions.

Big data fuels AI Fintech solutions by providing the extensive training data needed for machine learning algorithms. With access to datasets in the petabytes, AI systems can continuously learn and improve personalized services. The integration of big data and AI is opening new possibilities for real-time analytics, customized offerings, and more streamlined operations across the industry. 


Generative AI in Fintech

We're starting to see generative AI make a big impact in personal finance services. Up until now, customers have mostly gotten basic chatbots for basic questions. But generative AI promises much better conversations.

A few companies have launched real generative AI helpers you can talk to already. However, most big companies are still testing generative AI internally. Reports show people are eager to use services like ChatGPT though.

By the end of 2024, we'll likely see many banks, brokers, and credit card providers adopt this technology because of competition.

Generative AI will totally change how customers get support. Powerful AI helpers can explain complex topics simply, help with budgeting and planning, and provide personalized suggestions. In time, as the technology improves, people may turn to AI for most routine tasks instead of live agents.

Some financial services like insurance, advising, and retirement plans will likely be slower to change since relationships are so important there. But generative AI can also help a lot by lowering costs. While a few early users may emerge, widespread use in these areas is still a few years off probably.

As customer expectations rise with better generative AI elsewhere, slow companies risk losing business. Industry leaders need to start generative AI planning now to avoid playing catch up later in stressful ways. It takes time to get different parts of a company on the same page and write the first lines of code. The smart choice is to start as soon as possible rather than waiting. 

Overall, 2024 will mark a big shift as generative AI transforms personal finance through new conversational tools.

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Challenges of AI in Fintech industry

While AI brings great opportunities to improve customer service and make financial operations more efficient, implementing it comes with real hurdles. Companies adopting AI in Fintech need to work through technical, staffing, and ethical issues rather carefully.

On the technical side, integrating new AI systems with existing IT setups can be tricky. Financial companies have very complex infrastructure built up over decades. Making cutting-edge technologies work smoothly with these older systems is challenging. Studies show most banks struggle with this type of linking up.

Finding the right people to develop AI solutions also poses difficulties. You need data scientists, engineers, finance experts, lawyers and more - all skills in high demand. Reports find there is a big shortage worldwide of tech talent like data analysts.

Even after solving tech and staff problems, choosing how to apply AI demands thought. While better customer service and cost savings through AI sound good, prioritizing ways it helps humans directly is wise. Research shows many AI projects fall short as their goals were not well picked.

Once started, scaling up AI uses over time takes continued effort too. Early AI uses tend to automate simple repetitive work more than create real value. Evolving from that to deep learning for better predictions requires ongoing work and planning step-by-step growth.

Protecting customer privacy and building trust in AI are also important. With AI handling lots of personal financial data, strong security like encrypting information is needed to address privacy risks. Clearly explaining to customers how their data is used helps gain their confidence. However, most banking customers still feel AI uses are not fully clear to them.

Avoiding unfair outcomes from AI decisions poses an ethical problem. While laws ban discrimination, checking algorithm results for unwanted biases in loan approvals remains challenging. Strict guidelines on transparency and fairness demand diligence due to AI's complex fast-changing nature.

In summary, working through integration difficulties, staff shortfalls, wise goal-setting, gradual scaling up, and ethical priorities around privacy, trust, and biases present continuing hurdles to responsibly realize AI's benefits in Fintech.


AI in Fintech: use cases

AI is creating big changes in Fintech industry. It is being used in important areas like operations, customer support, security, investment advice, and more. Let's look at some of the key ways AI helps fintech.

One use is stopping fraud. AI programs can quickly review lots of transaction data to find unusual patterns and behaviors that might be fraud. This helps protect financial companies from tricks criminals use. AI also improves customer support with "chatbots". Chatbots learn to better answer common questions so people don't have to wait for a human. This makes helping customers faster and cheaper.

AI also decreases risks. By studying what customers do, AI models can better predict risks and make sure companies don't take on too much danger. This gives better forecasts. AI also automates tasks like making reports, streamlining work, and fixing fewer mistakes. In trading, AI programs immediately review market signs and make very fast, accurate trades to get investors better returns.

"Robo-advisors" show another use - giving personalized investment tips. This makes sophisticated strategies available to more people. AI integrations also optimize how resources are used, enhance customer experiences, improve analysis abilities, and build trust with transparency.

Specifically, AI chatbots are common for supporting customers. As they learn from conversations, they get better at responding and helping. This lightens workloads while boosting how satisfied customers feel. AI likewise customizes banking, investments, and insurance by understanding customer profiles and actions. 

Such personalization increases loyalty and trust. AI supplements traditional credit scores with a full view of reliability so more people can access loans. It also quickens loan decisions by swiftly screening applications.

Compliance is another big area where AI helps by continuously tracking regulation changes and ensuring adherence. Automated reports streamline work and save resources. AI integrated into fintech software and cloud platforms provides prompt data insights driving strategic and operational upgrades. Cloud infrastructure supports the cost-effective scaling and flexibility of these AI tools. 

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Is 2024 set to be a breakthrough?

There is strong evidence that 2024 will be a breakthrough year for AI in the Fintech sector. While conversational AI, such as ChatGPT, has generated much hype, its application within financial services has been limited to date. However, several factors point to the significant adoption of generative AI assistants within financial services firms in 2024.

Generative AI holds immense potential to transform how customers interact with and are served by financial institutions. In contrast to basic chatbots currently used, generative AI can understand natural language, hold conversations, and provide personalized advice and recommendations across a range of financial topics. It acts more like a true virtual assistant than existing options. This level of capability means generative AI could effectively replace the role of human advisors for many routine customer needs.

Pioneering firms like and Bunq have already launched generative AI assistants, demonstrating its feasibility. Wells Fargo and Dave are also developing generative AI, though current functionality remains basic. Sectors like banking, brokerages, and fintechs face strong competitive pressures and tend to adopt innovations quickly. Accordingly, these areas will likely see wider adoption of generative AI assistants in 2024, creating pressure for laggards to respond.

Other fields, such as insurance or financial advisors, are slower to change but would also benefit greatly from automating routine tasks through generative AI. While early exceptions may emerge, mass deployment in these sectors will probably take longer, with meaningful adoption extending beyond 2024.

Want to have an efficient, business-oriented design?

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The future of AI in Fintech

In summary, the growing AI in fintech market size and rapid pace of innovation point to how core technologies are revolutionizing financial services. 

Fintechs’ AI adoption allows for highly automated operations, personalized offerings, and improved efficiencies - significantly enhancing the industry's ability to serve customers. With its widespread benefits, AI will continue transforming fintech in promising ways for years to come. 

So, don’t lag behind - join the innovation.

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CEO and Founder of Merge

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

My mission is to help startups build software, experiment with new features, and bring their product vision to life.

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