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AI in the Finance Industry: The Road to Disruption
Irina Demianchuk
7minutes read
AI is gaining momentum in finance industry, both among startups and established brick-and-mortar businesses. Deployments of AI in finance and accounting successfully assist process coordination between branches and departments, reinforce security measures, fight fraud, enhance risk mitigation, drive the development of new products and services, and help reduce operational costs.
By using AI in finance, financial organizations can ensure they meet and surpass the demands of discerning customers through AI-based solutions or transforming entire workflows around artificial intelligence.
Using AI in finance for business process automation
AI in finance examples include lots of high-quality business process automation cases. The progress of machine learning in finance made it possible for software to improve with experience and move on from executing simple tasks to automating complex workflows that are typical of financial institutions.
Generative AI in the finance industry
Generative AI is rapidly transforming the financial sector, not just by automating processes but by fundamentally reshaping decision-making, risk management, and customer engagement. According to Precedence Research1, the market is projected to grow from $1.95 billion in 2025 to an estimated $15.69 billion by 2034 at a staggering CAGR of 26.29%, underscoring its profound impact. Financial institutions are embracing this technology to gain a competitive edge, leveraging AI to generate financial reports, assess risk with heightened accuracy, and optimize portfolio strategies with minimal human intervention.
Beyond automation, generative AI enhances financial forecasting by detecting subtle market trends, streamlining compliance processes, and creating data-rich models that support complex decision-making. A recent survey by NVIDIA highlights that 26% of AI applications in finance focus on natural language processing (NLP), empowering chatbots and AI-driven advisory tools, while 23% are used for portfolio optimization and recommendation systems. Fraud detection, a critical application, accounts for 22%, underscoring AI’s role in strengthening security measures.
The demand for AI applications in finance is accelerating. Companies are in search of trusted generative AI providers not only to refine investment strategies but also to expand the diversity and quality of financial datasets. By synthesizing new data points, AI models enhance predictive analytics, allowing institutions to anticipate risks and market fluctuations with greater precision. This ability to generate high-quality synthetic data also strengthens machine learning models, improving decision-making across trading, credit risk evaluation, and regulatory compliance.
With financial institutions prioritizing efficiency, security, and customer-centric innovation, generative AI is no longer just a tool for automation — it is a catalyst for industry-wide transformation. The next decade will see its continued integration into core financial processes, shaping a more adaptive, data-driven, and resilient financial ecosystem.
Financial process orchestration
Financial process orchestration is essential for any organization that aims to improve performance and capacity without inflating its workforce. By integrating specialized financial software development services, businesses can enhance their operational efficiency and develop customized solutions to meet specific financial needs. Partnering with financial software development services providers, like Oxagile, enables companies to leverage AI and automation to streamline complex workflows and deliver tailored financial solutions that optimize both cost and performance. An AI-based solution can be trained on internal, highly specific data, allowing small steps to be tailored and the end result to fit the exact needs of the business.
Artificial intelligence in finance can help bring previously disjointed tasks and processes together in a single, end-to-end financial platform for better visibility, tighter control, and smoother communication. Such systems can be scaled on demand without compromising accuracy or running into prohibitive operational costs.
Explainable AI (XAI) for compliance and transparency
As financial institutions deepen their reliance on AI-driven decision-making, the demand for transparency and accountability has never been greater. Explainable AI (XAI) is emerging as a crucial tool in bridging the gap between complex machine learning models and the need for interpretability in areas such as credit scoring, fraud detection, and risk assessment. Unlike traditional black-box AI systems, XAI ensures that financial organizations can trace and explain the logic behind algorithmic decisions, fostering trust among regulators, investors, and customers alike.
Beyond compliance, XAI serves as a safeguard against unintended biases that can arise from AI-driven financial models. By offering interpretable frameworks, it enables institutions to proactively identify and correct potential biases in lending, trading, and customer service algorithms, ensuring alignment with evolving financial regulations and ethical standards. This level of transparency is becoming a competitive differentiator, as customers and regulatory bodies increasingly demand fairness and accountability in AI-driven financial services.
The impact of AI-driven transparency extends beyond traditional banking, making significant waves in decentralized finance (DeFi). In this rapidly evolving sector, where smart contracts and algorithmic governance underpin digital transactions, XAI can help ensure that automated financial processes remain auditable and trustworthy. By demystifying complex AI operations, XAI not only strengthens regulatory confidence but also paves the way for more inclusive and ethical AI adoption across the financial ecosystem.
Credit scoring
Conventional credit scoring uses people’s credit histories to calculate the risk of underwriting a potential borrower. The process protects banks from irresponsible borrowers, but it can also hinder growth by denying loans to trustworthy individuals who don’t happen to have a credit history, e.g. young professionals.
AI-based credit scoring platform
This is where artificial intelligence comes in. AI-enabled credit scoring solutions employ predictive analytics and natural language processing to automate and scale the credit scoring process. Instead of credit history, they tap into available customer data — such as purchase history and social media activity — to assess how likely a person is to pay back loans in due time.
Assessment takes minutes instead of hours, enabling the bank to process more applicants, give credit access to more creditworthy clients, and raise profits while keeping the risks low.
How we can help
We apply AI in the finance industry to address a host of issues that financial organizations face, including fraud, performance bottlenecks, security gaps, system compatibility, usability, and more. Tap into our domain expertise and know-hows to stay ahead of the curve.
The rise of decentralized finance (DeFi) has introduced new challenges and opportunities for AI adoption. AI-powered analytics assist in risk assessment, fraud detection, and automated smart contract verification within blockchain-based financial ecosystems. Machine learning models analyze transaction patterns to detect anomalies, helping protect users from fraud and security breaches. Additionally, AI-driven trading bots are optimizing crypto investments by identifying arbitrage opportunities and executing high-frequency trading (HFT) strategies across decentralized exchanges.
Insurance solutions
AI in finance has proven an effective tool for insurance companies to enhance a whole range of tasks, from claims handling and underwriting decisions to customer service. Specialized insurtech solutions speed up claim classification and sorting, extract important data points, and build analytical models to detect anomalies that point to insurance fraud.
Besides fraud-scoring, machine learning is the basis of highly personalized insurance plan recommendations, providing clients with a quick and easy way to assess the merits of available options and keep premiums low.
Automated documents processing
As we mentioned above, AI is exceptionally good at processing text documents, far surpassing humans in speed and accuracy. Through continuous exposure to standardized documents such as loan forms, credit agreements, or contracts, AI applications in finance learn to effectively spot inconsistencies that could potentially lead to legal issues and create smart alerts about them. With tasks like regulatory compliance out of the way, human employees are able to spend more time doing creative, higher-value work and exploit new business opportunities.
Document digitization
For many major banks and financial institutions, digital transformation challenges start with a heap of legacy documentation stored on paper. Valuable historical data that could help train machine learning algorithms is rendered useless and massive effort is required to make physical documents readable for machines.
Thankfully, there are now computer vision solutions such as Parascript or Leverton that are able to accelerate and enhance this process, extracting data and organizing legacy paperwork hundreds of times faster than humans. Eventually, bank employees get access to smart search through digitized documents using content-aware filters.
Automated document classification and data entry
Using machine learning and natural language processing in digitization results in huge time and cost savings, better transparency, and eliminates human error where the cost of a misread number is prohibitively high.
Automated ESG scoring
For investors who stay conscious about social, environmental, and governance issues while building their investment portfolio, a company’s ESG score can be decisive. The score reflects how well an organization fares in such areas as climate change, board diversity, tax transparency, ethical sourcing, and more.
Source: Sensefolio ESG scoring process
AI scoring solutions provide unparalleled speed and repeatability of data analysis, enabling real-time monitoring of company activity across multiple sources. Supported by human-led research, AI-generated ESG scores help businesses and individuals more accurately assess the sustainability of their investment opportunities, improve long-term strategy with hard data, and connect with companies whose values match their own.
AI-powered hyperpersonalization
Artificial intelligence is enabling hyperpersonalized banking experiences by analyzing customer behavior, transaction history, and real-time financial data. AI-powered financial assistants can provide tailored investment recommendations, spending insights, and automated budget planning. Conversational AI and chatbots with sentiment analysis capabilities further enhance customer engagement by delivering personalized financial advice. By anticipating user needs and offering customized solutions, AI-driven hyperpersonalization helps banks strengthen customer relationships and improve retention.
Algorithmic trading
In recent years, the role of AI in trading has expanded significantly, with the market projected to grow from $21.59 billion in 2024 to $24.53 billion in 2025 at a CAGR of 13.6%, reaching $40.47 billion by 2029, according to The Business Research Company2. Like human traders, machines can learn to discover meaningful patterns by analyzing historical data. Combined with real-time market updates, this allows AI-driven trading systems to make accurate predictions about stocks, bonds, commodities, and currency prices under changing conditions.
As trading is heavily influenced by emotion, many automated solutions now employ sentiment analysis tools to process call transcripts, news stories, and social media posts, transforming unstructured data into actionable insights. AI also outperforms humans in detecting anomalies, enabling traders to capitalize on opportunities that might otherwise go unnoticed.
With 42.5% of enterprises in the EU adopting cloud-based solutions in 2023, AI-powered trading platforms are increasingly integrated with cloud infrastructure, enhancing accessibility, scalability, and efficiency. The industry is driven by innovations in algorithmic trading, predictive analytics, portfolio management, and market surveillance, with major players including Citigroup Inc., IBM Corporation, NVIDIA Corporation, Fidelity Investments, and DataRobot Inc. As AI continues to evolve, its ability to process vast amounts of financial data in real-time is transforming the trading landscape.
Security and fraud detection
With the advent of online payments and mobile banking, financial companies find themselves exposed to new security challenges. To successfully withstand malicious attacks 24/7, fintech systems have to stay up to date with cutting-edge technologies like artificial intelligence.
Biometric authentication and authorization
Biometrics solutions are popular with financial organizations that are looking for next-gen data and asset protections. The range of biometric deployments includes computer vision, voice biometrics, keystroke recognition, fingerprint recognition, and more.
While biometric systems are incredibly easy to use, they’re cheaper to support and harder to trick than password- or token-based authentication systems. Underpinned by lightning-fast AI analysis, these techs streamline authentication and authorization for company employees as well as clients across a wide range of devices.
Fraud detection and risk mitigation
High-performance solutions like DataVisor on the basis of unsupervised machine learning have evolved into powerful tools for addressing fraud and financial crimes. Trained on millions of past transactions and publicly available data, an AI solution is able to spot and flag suspicious spending behaviors early, helping to prevent credit card fraud while eliminating false positives.
AI-powered pattern detection
AI in the finance industry is also adept at financial risk assessment that informs strategy and investment policies. Companies apply AI-driven predictive analytics to generate detailed market reports, research interest rates, and forecast future sales.
ATM maintenance and security
While online threats tend to be in the spotlight with huge sums of money on the line, the bank’s ATMs are also a source of many concerns and vulnerabilities. Using AI and IoT tandem, banks can incorporate and scale predictive maintenance software across the entire ATM network. The solution tracks performance of every machine and issues alerts to maintenance staff before an ATM breaks down. Inactive ATMs lose a bank its service fees and create customer frustration, so keeping them operable 24/7 is in every bank’s best interest.
However, a perfectly functional ATM can still be a sitting duck for criminals. This is why one of the latest trends in banking is AI-enabled face recognition software. Installed into an ATM, face recognition ensures reliable identity verification of card holders and makes it virtually impossible to withdraw funds using someone’s else’s card.
Customer experience
With artificial intelligence in finance, AI use cases in finance show that service providers are able to learn more about their customers’ habits and preferences than ever before, and use that knowledge to personalize experiences.
Frictionless client support
A personal touch is very important for customer satisfaction, but until recently it was not possible for digital assistants to imitate human conversation convincingly. Advances of artificial intelligence in finance have enabled modern chatbots to understand complex questions, precisely categorize tickets, and even proactively address issues without human supervision.
Conversational banking solutions
Conversational banking software allows stepping up regular customer service through the use of NLP and AI to handle entire conversations with clients. This approach has a number of benefits.
First of all, letting conversational AI take care of repetitive and time-consuming tasks frees up valuable human resources for more sophisticated work and reduces service costs down the line. Faster response times and 24/7 availability improve customer satisfaction and create positive word-of-mouth among clients.
In addition, a user-friendly conversational interface helps users discover banking products and services through preferred digital channels and at their own pace. With seamless onboarding and proactive follow-ups on incomplete transactions, AI-powered banking assistants amplify conversion and keep drop-offs to a minimum.
Personal financial management
The ability of artificial intelligence to build comprehensive, up-to-date consumer profiles by sifting through unstructured personal data makes this technology a great fit for personal financial management solutions. AI-driven PFM apps like Mint adapt easily to mobile use, turning smartphones into customized financial advisors that you can carry in your pocket and consult on-the-go.
Beyond guiding users to more organized and money-saving financial habits, these programs cover entire financial journeys, simplifying processes like credit scoring, account opening, and loan processing.
Advanced wealth management
Advanced wealth management is another AI use case in finance. For high-profile bank clients, the data-crunching and behavior mapping capabilities of AI in finance are crucial for automating wealth management. For decades, curating a personal investment portfolio for a wealthy client has involved many hours of scrupulous research, compliance worries, and skyrocketing fees. Thanks to machine learning in finance, it is now possible to generate fact-based, fully compliant investment recommendations in a matter of minutes, at a fraction of the cost.
Readily available financial advice helps bank customers arrive at decisions quicker and with more confidence, while the banks boost customer loyalty and streamline managing a client base with different risk appetites.
Is artificial intelligence taking over fintech?
Artificial intelligence is not just integrating into fintech — it is redefining it. Financial institutions are leveraging AI to enhance decision-making, optimize risk management, and drive personalized financial experiences. Generative AI is accelerating financial forecasting and portfolio management, while explainable AI is ensuring transparency and regulatory compliance. AI-driven automation is streamlining operations, from fraud detection to credit scoring, improving efficiency while reducing costs.
However, AI is not a replacement for human expertise but a powerful tool for augmentation. While AI enhances trading, ESG scoring, and wealth management, human oversight remains critical to interpreting insights and mitigating biases. Financial institutions that strategically integrate AI while maintaining ethical standards and regulatory alignment will lead the industry forward. As AI adoption continues, its success will depend on balancing automation with accountability. Institutions that harness AI’s potential responsibly — ensuring transparency, security, and customer trust — will shape the future of finance.
