AI in Fintech: Driving Innovation and Security in Financial Technology

GenAI has been on the fintech industry's radar for just a year but has quickly become a top disruptive technology delivering significant ROI. Nearly half of financial services companies have already integrated AI into their operations. From analysing vast datasets and generating investment insights to providing personalised banking experiences, AI in fintech is quickly transforming the industry’s landscape.

Besides industry-specific use cases, fintech companies are racing to automate repetitive tasks, create new products faster, and cut operational costs using the power of AI.

Nvidia predicts that by 2025, all financial service providers will be using GenAI, a claim supported by Moody’s study showing that 90% of financial leaders are eager to adopt AI for risk and compliance solutions.

However, AI in fintech is a double-edged sword, as it comes with data privacy and compliance risks. As the industry embraces AI, the key question is: How can companies maximise AI's efficiency while ensuring it remains secure? In this article, we’ll explore AI's top applications in finance, its transformative impact, and the future of the industry.

Understanding AI in Fintech

AI in fintech is all about using large language models (LLMs) to make financial services better. They help fintech companies run more efficiently, deliver a better customer experience, and streamline operational processes. Machine learning, natural language processing, and predictive analytics, driven by AI, are revolutionising the way the financial world works.

Artificial intelligence in fintech is being increasingly implemented across risk management, fraud detection, customer service, and personalised financial advice. AI algorithms analyse large datasets to identify patterns that help predict credit risk and detect fraudulent activities in real time.

Here's a glimpse of how Bin Ren, Founder & CEO of SigTech, envisions the use of AI in wealth management:

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“Professionals will spend more time on understanding customer needs and translating them into questions for AI. The AI will do the groundwork of coming up with investment recommendations and solutions.”

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By 2025, fintech will see a surge of AI agents that will use real-time, reliable data to automate tasks across various business functions, including risk management, portfolio optimization, paperwork automation, and more.

The Role of AI in Fintech

The global fintech market is currently worth $340.1 billion and is growing with a CAGR of 18.5%. The share of AI in the fintech market size is $42.83 billion and is expected to grow with a CAGR of 2.91%.

Right now, AI accounts for 16.79% of the global fintech market, but that’s projected to drop to 7.20% by 2029. Why? Because AI is expected to become a core technology, as common and essential as computers are today.

While predictions can differ, this trend sends a clear message: fintech companies need to adopt AI now. Companies that delay risk falling behind competitors and struggling to operationalize their data in the future.

In its essence, AI in fintech creates competitive advantages for companies and increases the efficiency of processes across the board.

As of now, fintech in AI is predominantly used in operations and risk management:

Among financial services leaders questioned about the impact of AI on business operations, 42% claimed that GenAI created a competitive advantage for their organisation:

Zooming in on use cases, AI is being increasingly used In digital onboarding. Thus, in identity verification alone AI is projected to save over $900 million by 2028. As automated identity checks become more accurate, their costs continue to decrease.

Another area where AI stands out in fintech is customer service chatbots. These AI-powered bots can cut customer service costs by up to 80% while boosting response accuracy by 25%. They’re great at understanding customer sentiment and delivering personalised, efficient replies. This means faster problem-solving, more tailored interactions, and happier customers overall.

Now, let’s explore where AI in fintech makes the most difference.

Key Financial Niches and Services Where AI is Driving Innovation

Fraud Detection and Security

Generative AI is named as the biggest fraud trend over the next 3-5 years.

AI in fintech gives powerful tools to both fraudsters and defenders. Synthetic identity fraud, which already costs banks up to $6 billion a year, is a growing threat. The 700% surge in fintech-related deepfake incidents highlights just how urgent it is to deploy AI defences.

AI-based solutions are stepping up, especially during customer onboarding, where fraud often begins. Tools like Mastercard's Decision Intelligence analyse trillions of data points to predict and stop fraud in real-time, making fraud prevention more proactive than ever.

Yet, 31% of fraud teams still struggle to detect fraud during onboarding—a gap that AI tools are perfectly suited to fill.

Laura Barrowcliff,  Head of GBG Trust at GBG, a company developing global digital identity and fraud solutions, recommends the following:

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“To tackle fraud in an efficient way we recommend a multi-layered approach to fraud solutions, making sure there’s a robust identity verification process at the point of onboarding, fraud signal detection, and ensuring that you’re using AI in your technology to be able to identify fraud at scale”.

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Apart from that, sharing insights across sectors, leveraging advanced data models, and strengthening onboarding security will be critical in reducing fraud risks.

Transaction Data Enrichment

Raw transaction data often lacks the context users need to understand their spending. Transaction data enrichment solves this by adding useful details like merchant names, categories, and even location info. For instance, instead of seeing something cryptic like "AMZ*1234," you’d see "Amazon purchase," making it easier to recognize and track your transactions.

This comes with its set of benefits:

• Improved user experience: Users can easily understand their spending habits and identify charges without any confusion.
• Smarter Insights: Financial institutions can use enriched data to deliver advice on personal finance and spending, create tailored offers, and suggest products that meet customers’ needs.
• Increased Efficiency: Automating data enrichment saves time and cuts costs tied to manual processing.

With access to data about merchants and financial institutions AI can make sense of vague transaction descriptions. AI models can figure out who the merchant was, what kind of purchase was made, and where it happened. By doing this, AI ensures both users and fintechs get a clearer picture of transactions, making money management simpler, smarter, and more seamless.

Portfolio Optimization

Artificial Intelligence in fintech outperforms conventional portfolio optimization methods due to the ability to analyse massive amounts of data in seconds.

AI Agents help portfolio managers make sense of complex and ever-changing market conditions. They can spot patterns and predict market trends with impressive accuracy, giving investors an unprecedented edge in making timely decisions.

In a recent interview, Josh Pantony, CEO of Boosted.ai, an AI company empowering productivity for investment managers, explained how GenAI enables investment managers to make data-driven decisions:

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“Let’s say you’re doing M&A trade and you want AI Agents to constantly look for something that could increase the risk of that trade. Or, you’re interested in finding different types of corporate actions, and you want to know every time there’s a Dutch auction for example. You just teach AI what you care about, it looks through millions of words of context on a continuous basis and it notifies you when it finds it.”

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So, based on this context AI in fintech provides personalised recommendations that align perfectly with each investor’s objectives.

Another important factor for using AI for investment managers is that younger wealthy individuals (age 18-44) are more likely to trust managers that use AI in their workflow. 62% of Gen Z and Millennials are using AI on a weekly basis, and 35% of them said they will change their investment advisor if they’re not using AI.

Besides, AI doesn’t just improve strategy—it streamlines the workload. Tasks like compliance checks, tax optimization, and reporting can be automated, freeing up portfolio managers to focus on high-level strategic planning.

Document Processing

AI in fintech turns slow, error-prone document processing tasks into automated workflows. Using technologies like natural language processing and optical character recognition AI speeds up how financial documents are handled, at the same time helping companies improve accuracy.

A recent survey found that 80% of hedge fund managers use AI, with nearly 80% of them relying on it for processing, summarising, and conducting preliminary analysis of legal and financial documents.

The entire infrastructure is divided into data and AI layers.

The process starts with data aggregation from financial databases, scanned documents, and even emails. Once collected, the data gets cleaned, organised, and fed into AI models.

With the help of tools like Snowflake’s Document AI, data engineers can create data pipelines for continuous processing of financial documents of various types (invoices, finance statements, etc.)

AI is only as good as the data it works with. If the data is clean and structured, you’ll get useful insights. If it’s messy, you’ll get bad results—plain and simple. That’s why fintechs need a strong data pipeline in place for processing financial documents before adopting AI.

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Note: Vodworks helps businesses create a strong data foundation for AI document processing and other use cases in fintech. Their data engineers have streamlined data flows and optimised trillions of data points for various organisations. Schedule a free consultation to explore how Vodworks can assist with your AI transition.

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When a seamless flow of data is established, AI transforms raw information into actionable insights. These insights are stored in vector databases, making it easy for internal stakeholders to retrieve exactly what’s needed, when needed.

There are many more applications of AI in fintech, such as algorithmic trading, creating customer service chatbots, legacy code optimisation, creating tailored offers for customers, and more. As AI advances, its impact and use cases in fintech will continue to grow.

Benefits of AI in Fintech

Now it’s time to explore the most tangible benefits that Artificial Intelligence in fintech brings to companies:

Cost Savings and Revenue Increase

Despite the early stage of AI adoption, numerous companies report significant cost savings across several business functions. A research by McKinsey shows that Risk and Legal, Service Operations, and IT are some of the business functions that profit the most from adopting GenAI:

In general, 39% of respondents are seeing at least some decrease in operational costs, and 44% of them report revenue increases.

Agentic AI Helps Line Employees Make Informed Decisions

Line employees make countless important decisions every day, but they’re often overlooked when it comes to decision-making tools. Most companies focus on giving dashboards and data access to executives and managers but imagine how much more effective an organisation could be if every employee had tools to make smarter, faster decisions.

Agentic AI can become a second brain for every employee in your organisation. It connects directly to a company’s database to find the required data out of millions of data points and turn it into insights.

Unlike general tools like ChatGPT, Agentic AI is customised to integrate with your organisation’s data infrastructure and is tailored to the specific data your industry uses. This enables employees to quickly find the information they need and proactively act on daily tasks.

For a real-life example, see how a Dutch insurance provider automated the processing of 91% of motor claims.

Increasing Customer Satisfaction Satisfaction Score

AI in fintech already helps companies improve customer experience, boost retention, and build loyalty. Here's how:

Smart Recommendations

• AI predicts needs using financial history and life events, offering tailored solutions.
• 65% of clients want companies to adapt to their changing demands, and AI ensures timely, relevant advice like mortgages or savings plans.

Sentiment Insights

• AI detects behavior patterns and customer moods through sentiment and voice analysis.
• It helps agents respond better, closing service gaps and improving customer interactions.

24/7 Chatbot Support

• AI chatbots handle routine queries while agents focus on complex issues.
• Always-on support meets customer expectations for instant access on apps, websites, or messaging platforms.

Personalised Experiences

• AI creates complete customer profiles by combining data from multiple touchpoints, using both internally, and third-party acquired data.
• 80% of customers say great experiences matter as much as the product itself—AI-driven personalization delivers on this.

Omnichannel Consistency

• AI tracks interactions across channels, ensuring seamless transitions between platforms.
• Customers don’t have to repeat themselves, creating smoother experiences.

AI in fintech allows financial providers to meet rising customer expectations with faster, smarter, and more personalised service.

AI Fintech Companies Disrupting the Industry Today

With so many benefits and opportunities of AI in fintech, it’s no surprise that countless entrepreneurs and teams are jumping in to catch this wave and be the first to reshape the industry.

Let’s dive into some innovative solutions that are empowering fintech companies to harness the power of AI.

Yodlee

Yodlee is a financial data aggregation and analytics platform that drives insights for financial service providers. Yodlee provides fintechs with two essential tools for thriving in the AI era: access to vast, accurate financial data and advanced conversational AI capabilities.

Yodlee’s data platform allows aggregating financial data across 17,000+ data sources to build analytics for various use cases. With all this data at hand, fintech can easily build a comprehensive financial picture in a summarized format.

The platform’s conversational AI delivers personalised experience to consumers to help them reach their financial goals. Based on aggregated and cleaned data, Yodlee’s conversational AI acts as a financial assistant that gives financial advice tailored to each customer’s situation, alerts on financial activities, and enables clients to make informed financial decisions.

Boosted.ai

Boosted.ai is an innovative AI-driven platform for financial advisors that enables data-driven decision-making and client engagement.

The platform provides weekly or daily stock recommendations aligned with an advisor's investment style and complemented with plain-language explanations to better understand market sentiment and stay on top of early trends.

Besides, Boosted.ai provides detailed stock evaluations, including ratings based on fundamental, macro, and technical factors, along with predicted excess returns. This enables advisors to quickly assess the viability of potential investments.

Advisors can also monitor selected stocks from a single interface, creating multiple watchlists tailored to their needs. The platform offers insights from various AI strategies, providing diverse perspectives on each stock.

With access to over 150 proprietary strategies, advisors can find and subscribe to those that best fit their client’s needs, enhancing their investment process.

By integrating these AI capabilities, Boosted.ai empowers financial advisors to make informed, data-driven decisions, improve client communication, and streamline their investment workflows.

GBG

GBG is a global leader in digital identity and fraud prevention solutions, leveraging artificial intelligence to enhance customer intelligence and security.

GBG Trust uses AI to analyze vast identity data, detecting inconsistencies and anomalies to prevent fraud. This system delivers trust scores swiftly, ensuring secure and efficient customer onboarding.

With the help of AI algorithms, GBG's solutions perform detailed document image analyses to identify alterations, including face substitutions and deepfake attempts, thereby enhancing identity verification processes for fintech companies.

GBG's transaction monitoring system applies AI and machine learning to scrutinize transaction events and behavioral patterns, isolating suspicious activities without disrupting legitimate transactions.

Through these AI-powered innovations, GBG empowers financial institutions to make faster, more accurate decisions, bolstering security and trust in digital interactions.

Vodworks

Vodworks is a global software development company specializing in data engineering and AI enablement, helping businesses prepare and optimize their data for AI implementation.

With 150+ experts across 4 R&D centers, Vodworks supports companies in Europe, Asia Pacific, and the Middle East, ensuring their data infrastructure is ready for efficient AI integration. Serving industries like finance, telecoms, healthcare, and media, Vodworks team has extensive experience with industry-specific data.

Vodworks’ key services include:

• Data pipelines: Building systems for real-time data flow and centralized storage.
• Data warehouse: Setting up a single source of truth for accurate AI data processing.
• Data cleaning and normalisation: Using AI to normalize and prepare data for analysis.
• AI enablement: Deploying tailored AI models to meet specific business needs.

Leveraging their expertise in data engineering, Vodworks’ team helped True Digital optimize 5 trillion data points and reduce data infrastructure costs by 50%.

Reach out today to discover how Vodworks can help your fintech company implement AI.

Challenges and Limitations of AI in Fintech

Even though AI brings undeniable benefits, it also comes with its fair share of downsides and risks. Let’s see what organizations need to keep an eye on when deciding to implement AI in fintech.

Making AI Adoption a Strategic Priority

A major challenge in adopting AI in fintech is making this process a core part of the organizational strategy. Generative AI requires significant investment and commitment, but many financial institutions still struggle to prioritize it as a CEO-level focus.

AI in fintech succeeds only when the entire organization works toward its adoption. Without data from all departments and branches, the complete picture cannot be formed. If adoption is limited to one business unit, the impact will be minimal, as most data will remain siloed across other business units.

Moreover, without leadership buy-in, AI projects often face funding issues, adoption delays, and organizational bottlenecks. As seen from the best example, institutions making the most progress with AI have CEOs driving the effort, energizing teams, and aligning resources to move faster.

However, there are promising signs that this challenge is being addressed. In a recent roundtable of 26 banking executives, 60% said they now see generative AI as a top strategic priority.

Despite this progress, many organizations still need stronger leadership focus to fully embrace AI.

Dispersed Focus

Many financial institutions are failing to scale success with AI due to dispersed focus across multiple AI initiatives. Spreading efforts too thin by running multiple independent AI projects without a clear focus results in limited impact.

Finance, and fintech, in particular, is a complicated and highly-regulated landscape. Expanding generative AI into areas like credit risk, back-office operations, and KYC processes requires well-thought integration strategies.

Instead of running multiple initiatives, organizations need to to prioritize 1-2 high-impact use cases, such as customer service or fraud detection to see tangible progress.

Data Privacy and Ethical Concerns

As AI systems in the fintech market collect and analyze vast amounts of personal and financial data, ensuring data privacy has become a paramount concern. A 2021 survey by PwC found that 83% of consumers are concerned about how organizations use their data. In the fintech industry, where sensitive information is processed daily, the potential for data breaches raises alarm bells. For instance, Marriott International faced a massive data breach in 2018, compromising the personal information of over 500 million guests, which serves as a cautionary tale for all companies handling sensitive data.

To combat these risks, fintech firms must implement stringent data protection measures. The introduction of regulations such as the General Data Protection Regulation (GDPR) in Europe has prompted financial institutions to reassess their data handling practices. Companies like Revolut have adopted a proactive approach to comply with such regulations, ensuring customer data is handled ethically and transparently. As AI evolves, fintech firms must stay vigilant in their commitment to data privacy to maintain customer trust and comply with legal requirements.

AI in Fintech Future

The future of AI in fintech is being defined by the latest trends in data intelligence, predictive modeling, and AI-based simulators – technologies that allow fintech companies to understand, anticipate, and even simulate complex financial scenarios. These are more than just operational capabilities; they are the foundation for DeFi, blockchain security, and financial inclusion which we will explore further in the next paragraphs.

DeFi and AI Integration

IIntegrating AI in DeFi changes the way financial markets operate. DeFi platforms use AI to optimize trading strategies and automate processes and risk management. For example, Aave a DeFi protocol, uses AI algorithms to analyze lending patterns and adjust interest rates in real-time based on market demand. This real-time data intelligence allows users to make informed decisions, maximize returns, and minimize risks.

AI’s ability to process large datasets quickly allows DeFi projects to offer personalized financial products to individual user profiles. As these platforms grow the synergy between AI and DeFi will enable more people to access financial services and democratize finance globally. The future of AI in fintech will see more innovations in this space as the need for more efficient and transparent financial solutions grows.

AI in Blockchain and Cryptocurrency Security

Blockchain and cryptocurrency security is key and AI is critical to this. Fintech AI companies are building AI-driven solutions to detect fraud and secure user transactions. For example, Elliptic, a blockchain analytics company, uses machine learning algorithms to identify suspicious transactions and assess the risk of a cryptocurrency wallet. By using predictive analytics these systems can recognize patterns of fraudulent behavior and reduce hacks and scams by a significant amount.

Also AI based simulators are being integrated into blockchain systems to test and optimize security. These simulators use constraint propagation to model different attack scenarios and allow companies to identify vulnerabilities before they can be exploited. As blockchain evolves the partnership between AI and blockchain will strengthen the overall security and give more confidence in digital assets.

Driving AI Success Through Data Infrastructure With Vodworks

With the rapid adoption of AI in fintech, now is the perfect time to upgrade your data infrastructure and harness its full potential. Yes, building data pipelines and ensuring seamless AI integration can feel overwhelming. But with an experienced partner like Vodworks, simplifying these complexities becomes achievable.

Reach out today to see how Vodworks can help you stay ahead in the evolving fintech landscape.