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FinTech
Table of Contents
In recent years, AI has proved itself in redefining the finance industry. As customers raise the bar of their expectations towards services, technologies cater to this demand, making operations more flexible and faster. But, in the first place, artificial intelligence in FinTech made data the most valuable asset, opening up the door for further innovations in the whole industry. Let’s explore in detail what this shift is about.
As Deloitte suggests, there are five stages of AI adoption by a company: AI Aware, Localized AI, AI Aspirations, AI Company, and AI Competitor.
To stay competitive, financial companies need to embed AI as a foundational component in their organization and culture. Most of them are either becoming AI Aware or are using localized AI, yet the adoption levels are rising. According to McKinsey, nearly 72% of companies already use AI for at least one business task. Financial organizations keep pace: 36% of them stated that AI/ML technologies reduce their annual costs by more than 10%. Overall, the market value of artificial intelligence in FinTech is now estimated at $44.08 billion and projected to reach $61.3 billion by 2031. AI implementation is obviously moving into top gear — further, we’ll see what drives this growth.
AI’s adoption in the FinTech market has been caused by many factors in the industry and beyond.
Nowadays, organizations see the value of investing in efficient and fast big data platforms and cloud software. Thus, they also lay the foundation for developing, deploying, and scaling AI solutions, ending with adopting tech at lower costs.
During and after the COVID-19 breakout, a need for new technologies that made remote work, data processing, and services easier caused the sprawl of AI.
As customers now deal with masses of data from their banks and payment systems via email, messengers, and other programs, they expect a new level of service, with more structure and personalization. Financial companies, in their turn, can use the collected information to provide more personalized interactions. AI helps integrate, process, and analyze this data to help organizations with decision-making in their customer service.
As clients are expecting better services, FinTech companies and traditional banking are striving to be the exclusive providers. The one who knows how to leverage technologies and make the most of them wins. AI in FinTech helps optimize customer experience and make it more personalized.
Customers and financial institutions are not the only actors involved in adopting AI. We need the whole ecosystem to function so that new technologies sprawl and move to a new level.
Broadly speaking, AI technologies transform the industry across the board, from operations that were previously performed by people to personalization to enhancing data accuracy and fraud detection. Yet, we can have a closer look and break down the most popular scenarios of how AI can be implemented and the evident gains that companies get from it.
One of the most frequent scenarios for AI in FinTech is processing payments, both on personal and business levels. Delays and human errors can occur at each step of the process — data validation, authorization, clearing, or settlement. Systems powered with AI, including Robotic Process Automation (RPA) and Natural Language Processing (NLP), can analyze repetitive patterns and automate validations, mitigating these risks.
Financial apps with AI-powered chatbots and virtual assistants are go-to tools for financial planning services. Using machine learning (ML) technologies and data analytics, they analyze data like users’ goals and financial behavior, as well as risk tolerance factors for investment. As a next step, they handle a plethora of customer interactions in real time: manage portfolios, suggest customized investment strategies, and even execute trades.
Traditional credit scoring systems use the borrower’s credit history to do the risk assessment. AI models can enhance this process, using both traditional sources of data, like past credit history, credit score, and alternative ones, like the borrower’s utility payments, online behavior, and even mobile usage and income level. For instance, Upstart, an AI lending platform, analyzes education and employment to assess credit risk.
AI-powered chatbots and virtual assistants like AiseraGPT can handle thousands of queries simultaneously, including KYC, FAQs, and others. Through NLP and ML models, they can detect context and sentiment and provide personalized responses to customers. This means companies can delegate simple routine updates and issues to Conversational AI, but also more complex tasks such as facilitating transactions or providing tailored financial advice.
In FinTech, user behavior, such as spending patterns or app navigation flows, can be used to enhance digital experiences. AI tools not only allow tracking and interpreting data in real-time, but also predict customer needs. For example, Revolut uses behavioral analytics to offer personalized budget insights and anti-fraud alerts for its customers. In traditional banking, customers also prefer personalized services as a basis for trust, yet only 35% of traditional banks offer personalization that meets their needs.
Algorithmic trading platforms use ML algorithms to analyze massive datasets to make investment decisions, including identifying patterns, building portfolios and refining strategies, continuously adapting to changing market shifts. For instance, PitchBook, a research firm and financial data provider, built a tool called ‘VC Exit Predictor’ that provides investors worldwide with insights on startups’ potential growth prospects.
According to the IBM report, the financial sector is the most frequent target for cybercriminals. However, organizations using AI technology in their security protocols managed to lower data breach costs by $1.76 million compared with those who did not apply such protective measures.
A prominent example of a secure AI system is Mastercard’s Decision Intelligence platform. It uses historical transaction data to build risk profiles. Then, comparing the real-time user behavior, it flags any detected anomalies.
When it comes to the finance industry, generative AI carved out a separate niche. As the latest McKinsey annual research states, organizations are rethinking their strategies to actively implement Gen AI.
Investment banking is likely to gain a lot from generative models. This is especially true for activities like research, sales, and marketing, which require high output generation effort yet easy validation.
This can expand to coding with AI, like Goldman Sachs investment bank did, to speed up their developers’ work. Deloitte proves these efforts as effective and predicts a productivity rise of 27%–35% for the top 14 global investment banks using generative AI.
Paradoxically, Gen AI not only brings profit, but also loss. As more deepfakes and other roundabouts are created with the help of technologies and become self-learning, financial organizations need to step up their investments in anti-fraud detection tools.
The answer may be in training anti-fraud systems on synthetic fraud scenarios. For instance, the Feedzai platform uses generative AI to simulate fraudulent activities — the result is a 20% increase in fraud detection for a major European bank.
Though going in high gear, AI adoption is still far from perfect. Challenges range from data privacy issues to the high cost of integration and regulatory complexity.
Data is what financial institutions work with to a large extent. User credentials, personal IDs, transaction histories, and behavioral data — all this information is highly sensitive. If mismanaged or leaked, financial data can result in massive legal, reputational, and monetary losses. For these reasons, the finance industry is strictly regulated.
Also, the more AI models rely on large datasets, which is the expected course of events, the more vulnerable they become to cyberattacks. In the meantime, cyber threats are also evolving, demanding solid and secure infrastructure from companies.
To address these risks, companies must adopt a two-step approach: embed strong cybersecurity protocols into AI systems and ensure compliance with privacy regulations, such as GDPR, CCPA, PIPEDA, and PSD2.
FinTech companies can proactively strengthen their AI models against potential threats by employing adversarial training and model testing. This approach includes exposing AI systems to simulated attacks. Thus, in later, real-world scenarios, AI applications show more resilience.
Another effective approach is incorporating 'privacy by design' principles into AI systems from the start. These include data minimization (collecting only the data necessary for specific purposes), data anonymization, strict access controls, and regular data privacy impact assessments.
First, to adhere to compliance guidelines, companies should see them as a strategy. Second, they should align their technical, legal, and risk teams on the measures they should take to reach these standards.
For instance, Monzo and Revolut maintain dedicated compliance engineering teams that constantly align AI models with shifting regulations. To keep up with changes, it’s crucial to upskill internal teams and hire AI-savvy talent. Regular automated compliance checks and AI governance frameworks can help ease the processes.
According to a World Economic Forum (WEF) report, one of the challenges of adopting AI quickly is the need for a robust and expansive integration. For instance, banks still run rigid legacy systems lacking the infrastructure and architecture to support AI adoption.
For such core operations as underwriting, payment, or trading, an AI model should be deeply integrated into the current workflows. Otherwise, it won’t show value and won’t be scalable. Not all companies can afford this, opting for a straightforward implementation instead of a costly long-term solution. Moreover, AI must continuously adapt to changing business requirements and processes, which is impossible with partial or surface-level implementation.
Despite the gravity of these challenges, businesses do not need to start from scratch. First, they can adopt modular architectures without rebuilding entire systems. That means a company can transition parts of its legacy infrastructure to cloud-based systems that support AI models and embed AI components into certain functions, like fraud detection. Goldman Sachs, an investment company, followed this path, supporting AI models via APIs and microservices.
Low-code or no-code AI platforms such as DataRobot or Google Vertex AI can significantly accelerate AI development and integration. With such tools, FinTech companies can deploy AI models with minimal custom code.
Agile methodologies also help bridge the gap. Instead of developing an end-to-end AI solution and launching it all at once, companies can roll out features in an iterative way. For instance, a bank implementing AI-driven credit scoring can pilot the system on a specific product (like personal loans) before expanding to others. The model will evolve as it integrates deeper into workflows.
Strategic partnerships with AI vendors or FinTech startups can ease the burden. The above-named Goldman Sachs bridged its tech gap by investing in AI startups and banking collaborations. Thus, the company gained speed, flexibility, and expertise without a massive internal overhaul.
Experts predict many innovative ways in which FinTech companies can leverage AI. Here are some emerging and developing trends that have the potential to stay:
The FinTech AI ecosystem is expanding. Entry barriers become lower as AI-as-a-Service offerings increase. They provide scalable AI tools for fraud detection, customer service, risk assessment, which startups and small companies can adopt without upfront investments.
In its turn, venture capital investment in artificial intelligence has surged. The numbers speak for themselves: despite global economic instability, this sector remains a bright spot. Thus, in 2024, global VC funding for AI startups reached $131.5 billion, a 52% increase from the previous year. Generative AI Fintech companies raised over $20 billion by the third quarter of 2024.
Autonomous finance is an approach where AI platforms manage end-to-end financial processes with minimal human intervention, from budgeting and investment to compliance and customer service. With technologies such as optical character recognition (OCR) and intelligent character recognition (ICR), autonomous systems can analyze a user's spending habits, income patterns, and financial goals. Decision-making now becomes their job, which not only reduces operational costs but also minimizes errors.
As Fintech and AI become more integrated, ethical, security, and sustainability concerns arise more often.
Responsible and transparent AI practices are expected both from regulators and industry stakeholders. Some of the most important principles are addressing algorithmic bias (it happens way more often than desired) and ensuring that diverse stakeholders are involved in AI development.
In 2012, UNESCO introduced a global standard on AI ethics for policymakers, regulators, businesses, and civil society in 194 member states. ‘Recommendation on the Ethics of Artificial Intelligence’ can now be used as a source of truth and a checklist when any doubts arise.
Sustainability can be better explored and promoted with the help of AI technologies. For instance, algorithms can assess environmental risks and identify sustainable investment opportunities.
Decentralized finance (DeFi) is another area where AI is making inroads. When complemented with AI technologies, blockchain platforms make financial services more secure and efficient. Lending and asset management are automated, while users enjoy swift functionality.
ML and generative AI are constantly revolutionizing FinTech. Beyond chatbots and personalization, which we already mentioned, these technologies are being applied in various innovative ways.
Generative AI enhances compliance and accompanying costs. A prime example is Fynhaus, a RegTech solution used for automated compliance checks. In 2024, the platform helped reduce regulatory fines by 80% for its clients, cut their operational costs, and speed up onboarding for the new clients.
Generative AI Fintech solutions enable institutions to check their risk tolerance by simulating various market scenarios. Ayasdi, an AI platform, assisted a major investment bank by modeling extreme events like the 2008 financial crisis. The results were impressive: developed more resilient financial products and reached a 30% improvement in risk management strategies.
Blockchain technologies ensure that transaction records are tamper-proof and transparent, while AI complements them by detecting anomalies and predicting fraudulent activities. Together, they bring in a robust framework for secure digital transactions, smart contracts, and regulatory compliance.
This relatively new and rapidly evolving area of computer science becomes even more solid when combined with AI. This technology blend helps process complex datasets at unprecedented speeds. Some financial institutions like JPMorgan Chase are exploring quantum solutions for predictive modeling and machine learning. More advancements are likely to follow.
The integration of AI in FinTech is reshaping the industry across the board, from payment automation to investment strategies. As adoption grows, companies face real hurdles: data privacy, regulation, and integration costs. But the upside is worth the challenge: smarter personalization, faster operations, and stronger risk management.
Whether you're a fast-growing startup aiming to scale or a traditional financial institution looking to modernize, the right AI solution can completely transform your operations. At NEKLO, we combine deep expertise in finance and AI to help you build securely. Reach out to discuss your project and build solutions that drive real results!
US-based and operating globally, NEKLO spearheads digital transformation initiatives for businesses worldwide. We deliver software solutions complying with your market and legislation.
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