AI in FinTech: Benefits and Obstacles You Should Know About in 2025

AI in FinTech refers to the use of artificial intelligence technologies to improve financial services, automating and personalizing them. It involves utilizing smart systems that learn from internal business data to make your financial processes smarter, faster, safer, more efficient, data-driven, user-friendly, and less prone to human error. Custom AI models can analyze vast amounts of financial data, detect patterns, predict outcomes, and make decisions without human intervention.

AI in FinTech industry is growing fast and demonstrates incredible performance and revenue indicators, proving its great potential. We’ve collected the most telling statistics that show the AI and FinTech market perspectives:

• Future Market Insights reported that the global FinTech AI market was $13,515.7 million in 2024
• According to the same Future Market Insights, overall AI in FinTech market is expected to be $58,701.4 million by 2034, reflecting a robust CAGR of 15.9%
• CoinLaw revealed that approximately 40% of loan approvals now incorporate AI analysis, reducing processing times by up to 30%
• According to Reuters, the bank's AI tools contributed to a 20% increase in asset and wealth management sales between 2023 and 2024, with expectations to expand client portfolios by 50% over five years

AI in FinTech gives financial institutions a lot of advantages, from lower operational costs to streamlined decision-making. Also, it brings gains for clients such as faster service, better personalization, and more. Now, let’s take a look at the benefits for both your FinTech businesses and your clients.

Here are the main benefits FinTech companies get when they implement AI tools:

Your clients also receive many advantages when you use AI when delivering financial services, since artificial intelligence greatly improves service speed and quality. There are the main:

FinTech AI tools mainly help in three aspects of your business: customer experience, employee productivity, and process optimization. Let’s look at the top 3 AI use cases in the FinTech industry according to these aspects.

AI chatbots and virtual assistants reshape how FinTech companies interact with users. They deliver faster answers and adapt responses in real time. By removing friction and personalizing interactions, firms increase engagement and build trust. AI chatbots interpret both customer and internal team questions, analyze the context, and generate accurate, conversational replies. In addition, AI-managed portfolios enable platforms to create personalized investment strategies tailored to specific financial goals.

Let’s look at the company that has implemented AI in its financial services and has already enhanced its customer experience:

• NatWest Group. They are a UK-focused bank serving over 19 million customers, with businesses across retail, commercial, and private banking markets. NatWest Group has implemented the models that analyze customer behavior and provide business insights based on it. These AI models help the bank deliver personalised support that customers want and need, and detect if customers might be in the process of being scammed.

AI helps teams extract value from data that would otherwise go unused. Many financial firms store years of documents, filings, and emails, yet struggle to turn them into actionable insight. Moreover, Fintech and AI solutions automate the discovery process, freeing up analysts and compliance teams to focus on decision-making.

Teams now use FinTech AI tools to build structured data on demand, competitor landscapes, product catalogs, and relationship maps from unstructured sources. Such an approach allows businesses to quickly respond to changes in the market and stay ahead.

Let’s look at the company that boosted financial team productivity by implementing AI in its financial services:

• Deriv. They are one of the world’s largest online brokers. Deriv implemented a generative AI solution to integrate and search information from multiple internal sources, including Slack, Google Docs, and others, without the need for machine learning expertise. Thanks to it, the company reduced new hire onboarding time by 45% and accelerated recruiting tasks by 50%.

Workflow automation is one of the most valuable AI applications in FinTech. It streamlines complex documentation tasks and supports product development. Fintech entities use AI to handle routine but essential work that once slowed down growth.

Firms use AI FinTech to automatically draft research reports, loan documents, insurance policies, and regulatory communication. AI also powers new product features like natural language stock screening for wealth advisors and clients. These innovations reduce time-to-market, support compliance, and drive competitive advantage.

Here is an organization that integrated AI into its services and optimized financial workflows:

• Crypto.com. They are a cryptocurrency company with approximately 100 million users across 90 countries that offers a trading platform, derivative exchanges, and more. Crypto.com has implemented generative AI to perform high-speed and multilingual sentiment analysis of the crypto market. As a result, the company is able to provide accurate and localized real-time market insights to its users, thanks to streamlining financial workflows and improving the user experience.

The process of integrating AI in FinTech is complex and involves many aspects, starting with choosing a reliable vendor and aligning on an AI strategy and ending with solution development, testing, and deployment. To simplify it for you, we have prepared a step-by-step guide on how to integrate AI in your financial company.

Start by identifying where AI can bring the most value to your financial business. Focus on the outcomes you want to achieve, whether it’s improving customer engagement, simplifying internal workflows, or streamlining operational processes. A clear set of goals helps align future development with tangible business impact.

To move from idea to implementation, assess the technical feasibility of your AI FinTech initiative. It includes reviewing the quality, structure, and availability of your existing data, since successful AI models rely on strong, relevant datasets. At this stage, you should rely on your tech partner with a strong portfolio and client reviews on trusted platforms like Clutch. Your vendor will guide you through this assessment, helping evaluate your current infrastructure and prepare everything needed to train and deploy AI effectively.

Partner with your FinTech software development vendor to build a Proof of Concept (PoC) that is a lightweight version of the solution focused on core functionality. Vendors often deploy PoCs on cloud platforms like AWS to ensure speed and scalability, also integrating AI services to validate critical machine learning components early on. For example, they can use Amazon SageMaker for model training, AWS Lambda for automated responses, or Amazon Rekognition for document verification and biometric ID checks.

During this stage, your vendor gathers feedback from the people who will actually use the solution, such as your customer support team, loan officers, or financial advisors, depending on who your product must help. Their input helps determine whether the tool improves daily workflows, solves specific pain points, and is worth scaling into a full-fledged product.

Based on validated use cases and available data, the team refines the model, ensures stable performance, and integrates the solution into your business infrastructure. The goal is a seamless transition from PoC to a production-ready system that delivers measurable results. Once deployed, your AI solution will deliver real-time value, ready to support business growth from day one. A structured launch plan and ongoing performance monitoring ensure your model remains reliable, secure, and aligned with shifting business demands.

Despite the benefits of AI in Fintech, there are also obstacles that you need to solve. We’ve outlined the main obstacles to integrating AI in your financial services and ways to cope with them.

Challenge: AI applications in FinTech must comply with a complex mix of global and regional regulations (Source: Research by Wiley). Any lapse could lead to legal exposure or data breaches. Entrepreneurs often struggle to align AI tools with both global regulations, like FATF recommendations or ISO/IEC 27001, and regional laws, such as GDPR and MiFID II in the EU, or CCPA and GLBA in the USA. This challenge intensifies as businesses scale across markets with different legal frameworks.

Cleveroad’s solution: We design AI with compliance in mind from day one. Our team has deep experience building FinTech AI tools that meet global and regional standards, including GDPR, PCI DSS, PSD2, AMLD, MiFID II, ePrivacy, and eIDAS (QES, SES, AES). Whether your product involves digital signatures, KYC, or CLOB-based trading, we ensure your AI integrates securely and lawfully across all your operational regions.

Challenge: Using AI with FinTech means handling sensitive financial and personal data. A single weak point in data security can damage user trust and trigger regulatory fines. As more AI use cases in FinTech emerge, risks grow.

Cleveroad’s solution: As a company upholding ISO/IEC 27001:2013 certificate for security management standards, we apply clear and documented processes to manage risks, control access to sensitive data, and prevent breaches. For every FinTech project, we implement such security measures:

• Security testing
• Malware & fraud protection
• Vulnerability assessment
• RegTech

With our assistance, you can safely leverage artificial intelligence for FinTech while meeting security requirements and customer expectations.

Challenge: AI projects demand serious investment, especially in FinTech, where companies must strengthen infrastructure and secure sensitive data. Moreover, companies often face overinvestment by implementing complex solutions without proven value, forced to incur extra costs again and again until they find the perfect AI solution.

Cleveroad’s solution: We reduce the risk of ineffective investments starting at the POC. Such an approach allows you to test the value of AI for your FinTech business by implementing only a partially functional product and significantly reducing the initial investment to identify the right AI solution and assess its business impact.

Moreover, we also provide flexible cooperation models for AI development and deployment in FinTech, giving you the choice between custom development, a dedicated team that fully manages all processes, or IT staff augmentation to extend your AI dev capabilities with several specialists working under your management.

Recently, we’ve delivered IT staff augmentation services for our client in the FinTech industry from Denmark, named Penneo. They have about 3000 clients within various industries, including Audit and Accounting, Finance and Banking, and Real Estate. They also cooperated with well-known companies such as Deloitte, KPMG, Jyske, and others.

Penneo partnered with Cleveroad to quickly augment their internal platform team with two senior DevOps engineers. Throughout the collaboration, onboarded Cleveroad DevOps engineers handled diverse tasks to support cloud infrastructure according to our client’s practices, using the Infrastructure-as-Code approach and Terraform as an IaC tool.

See what Hans Jørgen Skovgaard, CTO at Penneo, says about cooperation with Cleveroad.

Challenge: Building and deploying AI in FinTech isn’t simple. From data processing to training custom models, the technical depth required often overwhelms internal teams. Even firms familiar with the benefits of AI in FinTech may hesitate due to the complexity of implementation.

Cleveroad’s solution: We bring high-skilled AI engineering to your FinTech projects. Our team creates custom AI solutions that automate workflows, predict user behavior, and enhance decision-making. Clveroad specialists specialize in NLP, smart chatbots, predictive analytics, and generative AI models tailored to FinTech.

Integrating AI with FinTech offers transformative value, but only when challenges like regulation, security, cost, and complexity are addressed. At Cleveroad, we deliver fintech artificial intelligence solutions that are compliant, secure, and scalable. Let us tell you more about how we can help you.

Cleveroad is a FinTech software development company with 13+ years of domain experience. We also have deep expertise in AI development, helping our clients implement advanced AI-driven solutions tailored to their unique needs. Our team develops various AI solutions for FinTech, including AI-based fraud detection and risk management, automated trading software, personalized finance assistants, NLP-powered chatbots for customer support, and much more.

Choosing Cleveroad to implement artificial intelligence in your FinTech business, you’ll get:

• Comprehensive AI development service including AI consulting, custom AI application development, model training, evaluation, and fine-tuning
• A partnership with a team of AI engineers that is skilled in the latest AI technologies, including machine learning, natural language processing, and computer vision
• Free Solution Workshop stage to align your FinTech AI needs with the technical realization within a comprehensive AI implementation roadmap
• AI POC services to validate and refine the compatibility and performance of a new technology solution with your FinTech business IT ecosystem
• Partnership with an ISO-certified company that strictly adheres to ISO 27001 security standards and implements ISO 9001 quality management systems
• A high level of expertise in providing cloud services is proven by receiving Amazon Web Services (AWS) Select Tier Partner status within the AWS Partner Network (APN)
• A partnership with a company that leverage advanced AWS tools like SageMaker and Bedrock to build secure, scalable, and AI-ready cloud solutions

We have hands-on experience smoothly implementing AI in the FinTech industry, and to prove our skills, we would like to present one of our recent case studies.

Cleveroad developed an investment management system for our client from the UK, who is a conglomerate with 22 years of experience in property investment. Their goal was to create a scalable ecosystem that would serve individual and institutional investors, advisors, and property developers, all within a single platform.

To address our client’s business needs, we designed and developed a comprehensive system that included mobile apps for investors and advisors, a web portal, and an admin dashboard. The Cleveroad team built the platform with security and compliance at its core, including a robust KYC module with multiple verification options. The platform covers investment flow management, adapting users' access according to their role.

Also, as part of the investment platform, we introduced an AI-based Investment Advisor — a smart feature designed to support investors in making informed decisions. Leveraging user profile data, investment history, risk appetite, and current market trends, the AI module suggests optimal investment opportunities within the platform.

As a result, our client received an investment management system on time and within budget. Over the first operational year, investors contributed £164.4M through the platform, and revenue reached £7.6M and grew to £9.3M the following year. The platform not only simplified the investment process but also significantly boosted user engagement and investor trust.