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fintech use cases AI for Fintech Software: Top Use Cases for Business

AI for Fintech Software: The Best Use Cases for Your Business

Aug 22, 2024

18 mins read

The last decade has brought fintech to the forefront of the financial services industry. In particular, in 2023, publicly traded fintech companies represented a market capitalization of $550 billion, which was twice bigger than in 2019. According to McKinsey, this exponential growth witnessed a slowdown in 2022, and in the exact same year, Gartner’s Modern Digital Finance Talent Survey revealed that 71% of finance professionals believed that utilizing the latest technology was essential for their companies and their continuous business growth.

Artificial intelligence (AI) is the most vivid example of such a technology ensuring the continuous business growth of the fintech industry. In particular, the AI in fintech market has already reached 44.08 billion and is growing at a compound annual growth rate (CAGR) of 2.91 percent. The market size is forecasted to exceed 50 billion U.S. dollars in 2029.

ai in fintech market
Recent statistics on AI usage in FinTech

Today, we will explore the most useful ways of implementing artificial intelligence software for fintech. This article explains the most popular AI use cases, the value such solutions bring to business, as well as common fintech AI challenges and ways to mitigate them.

Top Fintech AI Use Cases

According to recent projections, over the next four years, technologies like AI will impact more than 26% of current financial industry service roles. No wonder that AI is the most popular technology in fintech software development.

AI for fintech
The demand for innovative technologies in the FinTech domain

Artificial intelligence can cover a variety of use cases, but we can roughly divide AI fintech features into three categories:

  • AI-powered workflow and customer service automation
  • AI for analytics, forecasting, and risk assessment
  • AI features that cover security and monitoring needs
fintech ai
Most general purposes for using AI in fintech

So, without further ado, let’s have a more detailed overview of the most common AI fintech use cases.

AI-powered automation for fintech

The financial services industry is overwhelmed with routine processes that may be consuming in terms of both time and effort. It is reported that 30.4% of fintech company executives believe that AI-powered automation has increased their profitability and performance by 11% and more. Therefore, one of the most common AI use cases in fintech is automation which accelerates workflows, improves their efficiency, and reduces the need for human involvement.

fintech AI
AI for FinTech automation

Acceleration for finance transactions

Commonly used in: online payment systems, digital banking apps, and e-wallets.

AI-powered automation helps payment systems process transactions faster and more efficiently, which significantly boosts user satisfaction. The point is that AI/ML scripts automate and can instantly process many workflows that are, usually, responsible for long transaction time. ML scripts can automatically adjust all transaction data to industry standards, such as ISO 8583 for card transactions and the ISO 20022 universal standard. This simplifies transaction management, categorization, and processing, which translates into greater transaction speed and traceability.

AI algorithms can also remove or correct any errors or inconsistencies in transaction data, run raw data transformations, control transaction accuracy, automatically adjust transaction parameters, enhance decision-making, validate transactions, etc. With such automation and automated performance tuning, many providers can run transactions that once took 1-3 days within seconds. Such solutions improve customer satisfaction with received services and allow both financial institutions and their clients to manage and track transactions more efficiently.

Customer communication and support

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, e-wallets, fintech CRMs.

A study conducted by Boston Consulting revealed that 43% of customers are likely to switch banks due to dissatisfaction with digital customer service. Customer support and communication have always been a big problem for financial institutions because they are typically time-consuming and require significant human resource allocation. AI integration into fintech apps allows such companies, ranging from digital banks to providers of personal budgeting apps, to automate this process. 

An AI employee or an ML-powered chatbot for fintech can be built with large language models (LLMs) or through the integration and configuration of off-the-shelf solutions, such as Azure AI Bot Service. Such chatbots cannot replace a customer support team completely, but they can handle basic questions and serve as a knowledge base for the customers, simplifying the work of your customer support team. In addition, AI chatbots can aid lending, investment management, and banking platforms by gathering and analyzing relevant customer data and running customer pre-screening.

Documentation processing and management

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, e-wallets, fintech CRMs.

AI helps different financial institutions process various documentation and generate contracts, which is just another time-consuming routine in the financial domain. ML scripts can automate this process. By integrating such algorithms into your fintech app, you can accelerate and simplify the generation, extraction, verification, and categorization of data from various documents. An ML-powered computer vision enhanced with natural language processing (NLP) and optical character recognition (OCR), can deal with any type of financial document, ranging from identification proofs to sophisticated invoices and detailed contract agreements. In addition, AI can provide you with the most fitting contract templates, which can save your employees much time nad effort. Such innovations translate into greater workflow efficiency, accuracy, and cost savings.

Automated claim and application processing

Commonly used in: insurance apps, P2P lending platforms, digital banking solutions, fintech CRMs.

Apart from standard financial documents, AI can automate customer claim processing. This feature is especially useful to insurance platforms. AI-powered systems enhanced with computer vision and analytical scripts can instantly validate the claim by cross-referencing it with policy details, historical data, and predefined rules to ensure its legitimacy. Such a feature can save insurance agents and other financial service providers hours required for manual information entry and analysis. In insurance apps, AI-powered analytical scripts can also access the severity of the claim, predict the likely payout based on historical data, and expedite the approval process for straightforward cases, which also implies great savings in terms of time and resources. Finally, machine learning algorithms can analyze the claim for anomalies or suspicious details, which serves as an additional safeguard against fraudulent activities.

Fintech analytics, forecasting, and risk assessment through AI

The global market for AI analytics is projected to show a compound annual growth rate of 22.60% between 2024 and 2034. As analytics remains one of the most common AI use cases for different businesses, fintech businesses widely apply ML models for analytics, forecasting, and risk management. 

AI for fintech
AI for analytics, forecasting, and risk management in FinTech

Financial forecasting and business intelligence

Commonly used in: finance planning and budgeting apps, tax calculation solutions, investment management platforms, digital banking apps, invoice factoring solutions.

Forecasting is one of the most popular fintech AI use cases with 44.30% of fintech companies using ML-powered predictive analytics. Such functionality is vital for both financial institutions and users of personal fintech apps because it can mitigate risks and minimize financial losses. Accurate forecasts help digital banks and companies engaged in invoice factoring predict market trends that will impact their vital operations and cash flows. AI-powered forecasting also helps users of investment management solutions allocate their resources more efficiently and anticipate the returns on their investments.

Such features perfectly work in combination with AI-powered business intelligence (BI) scripts that process different datasets to provide actionable business insights. The company can identify flaws in their operations and business strategy, identify opportunities, and anticipate the outcomes of different initiatives and business approaches. 

Such AI features are also crucial for budgeting and tax calculation apps, as AI can deliver precise financial insights and forecasts by analyzing extensive data on financial habits, incomes, expenses, regulations, and market trends. Overall, AI scripts for predictive analytics and BI, based on vast amounts of user, transaction, and market data, help fintech businesses boost their efficiency and improve market positioning dramatically.

Risk assessment and management

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, fintech CRMs, e-wallets.

A study conducted by KPMG revealed that 68 U.S. fintech executives plan to use generative AI in handling compliance and risks. Such solutions can process vast datasets to identify potential threats and predict future risks with greater accuracy. Well-trained ML scripts for data analytics and predictive modeling can precisely evaluate and manage risks associated with investment, insurance policies, lending, and other critical financial workflows. Such software assigns risk scores to fintech customers or policies based on AI models’ predictions. This enables financial businesses to predict, identify, and mitigate risks, as well as establish risk policies, far more efficiently than relying solely on human employees.

Automated portfolio generation

Commonly used in: investment management platforms.

The global market for digital investment is expected to grow at a staggering annual rate of 6.19% from 2024 to 2027. The demand for investment fintech solutions is high, and AI-powered analytics for investment portfolio and risk management can improve the efficiency of such platforms. By analyzing massive datasets, including market conditions, asset performance, and individual investor behaviors, AI for fintech can identify patterns and trends that human analysis might miss. Such functionality fits a great range of cases, from mortgage to invoice factoring. 

ML scripts enable real-time portfolio adjustments, predictive risk assessments, and personalized investment recommendations, leading to more resilient portfolios and better-aligned risk-return profiles. This feature significantly increases user satisfaction and allows investment platforms to achieve greater efficiency in their operations, which positively impacts their market reputation

Financial planning advisory

Commonly used in: finance planning and budgeting apps, tax calculation solutions, investment management platforms, digital banking apps.

According to Accenture, 55% of financial advisors believe that AI will have either a transformative or revolutionary effect on the future of financial advice within the next three years. Diverse fintech apps can benefit from an AI-powered advisor that leverages advanced algorithms and machine learning to provide personalized and data-driven financial advice. 

 

AI algorithms collect and analyze user’s financial data, such as income and spending trends, investments, and tax history. AI then uses this data to generate tailored recommendations, such as optimal investment strategies, tax-saving opportunities, and budgeting tips. Such efficiency helps different fintech apps, especially budgeting solutions and investment platforms, improve user value, which translates into better customer retention and acquisition.

Credit score management

Commonly used in: digital banking apps, insurance applications, P2P lending platforms, invoice factoring apps, fintech CRMs.

Apart from providing general risk assessments, fintech AI algorithms can assign and manage credit scores to different users. Such tools make decisions formed from the user’s following data:

  • Total income
  • Transaction history
  • Credit history
  • Professional credentials and qualifications
  • Analytics on the user’s behavior.

An ML model uses statistical methods and calculations to create a credit score based on the information mentioned above. The score is very sensitive, individualized, and dynamic, as it can change depending on various real-time factors. It is a very useful feature for digital banks, lending platforms, and other financial platforms that need to run customer screening for risk mitigation.

AI fintech security and monitoring

Around 76% of U.S. fintech company executives use or plan to use AI to enhance security and prevent fraud. Security and compliance are major concerns in the industry, and AI offers financial institutions significant support in protecting their operations and ensuring they remain compliant with various regulations and standards.

fintech security
AI for FinTech security

Compliance assistance

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, e-wallets, fintech CRMs.

AI features help fintech companies to comply with regulatory requirements and rules, such as Payment Card Industry Data Security Standard, California Consumer Privacy Act, (NIST) Cybersecurity Framework, etc. Machine learning scripts bring significant levels of automation to this domain, as they can continuously monitor transactions and communications. If properly configured, such a solution can flag any activity that may breach compliance regulations. 

Besides, AI-powered natural language processing (NLP) tools can be used to analyze whether policy documents and communication meet legal requirements. Financial institutions can also use AI compliance features in their CRMs to track changes in regulatory landscapes. As a result, all compliance protocols are updated automatically. With such scripts, fintech companies can avoid many legal threats associated with noncompliance and ensure customer trust with their strictly compliant workflows.

Fraud and anomaly detection

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, e-wallets.

According to the PWC report, AI scripts can improve fraud detection rates by 50% compared to traditional rule-based systems. Payment providers use AI-powered anomaly detection for transaction processing software. These AI/ML scripts identify patterns and deviations in transaction data, highlighting unusual or potentially fraudulent activities. Such a script needs access to the collection of large volumes of historical transaction data. It establishes the baseline of normal behavior. The AI models are then trained to recognize these patterns and detect anomalies by continuously monitoring real-time transactions. When a transaction significantly deviates from the established norm, the system flags it for further investigation.

Biometric authentication

Commonly used in: online payment systems, insurance applications, finance planning and budgeting apps, P2P lending platforms, invoice factoring apps, tax calculation solutions, investment management platforms, digital banking apps, mobile point-of-sales apps, fintech CRMs, e-wallets.

According to the Biometrics Institute, AI usage in biometrics has more than doubled from 8% in 2022 to 19% in 2023. AI is rapidly taking over digital identity’s dominant place in biometrics, and the fintech industry is also affected by this trend. Many financial institutions are already using AI-powered face, voice, and print recognition for fintech app authentication and authorization. The main advantage of ML models, in this regard, is that they can be trained to improve the efficiency of biometric authentication over time. As a result, ML models are resistant to the ever-evolving specter of fraudulent activities that may include attempts to trick face recognition scripts with deepfakes.

AI-powered biometrics can be implemented in a fintech app through the integration of custom scripts or with various AI services, such as Azure AI Face, Azure AI Vision, or Amazon Rekognition. With such tools, fintech companies can improve their apps’ resistance to cyberthreats and fraudulent activities dramatically.

Challenges of AI Adoption in FinTech

Surprisingly, despite the great potential of fintech AI, only 30% of organizations report that they’ve realized significant financial benefits from artificial intelligence. The problem is that many companies fail to cope with common fintech AI implementation challenges. These are the most common challenges to implementing AI for fintech and suggestions on how to deal with them.

AI challenges
Challengs with implementing AI in fintech

Legacy software and infrastructure

In recent years, the problem of legacy infrastructure was one of the major factors hindering the development of new technologies in fintech. In particular, a study shows that about 60% of digital banks and financial institutions are still using legacy systems, which are often seen as barriers to digital transformation. Common reasons for this problem are the lack of flexibility and technological experience.

To deal with the problem of legacy software and infrastructure, fintech businesses need to embrace innovation on the architectural level of their solutions. A popular way is relying on the cloud infrastructures of the world’s biggest technology providers, such as Microsoft Azure, AWS, and Google Cloud Platform. By implementing cloud-based systems, financial technology companies get architectural flexibility and scalability while keeping their solutions cost-efficient. In addition, the leading cloud platforms provide a great array of AI services allowing the developers to quickly and smoothly enhance fintech systems with AI tools and custom ML scripts. It is also essential to rely on a team with solid AI development expertise capable of planning and building such a cloud-based solution enhanced with ML scripts.

Lack of expertise

A recent survey showed that 54% of fintech industry respondents identified the shortage of  talent as their biggest obstacle to AI implementation​. This is especially relevant to the cases when FinTech companies require their existing specialists without AI-specific experience to embrace this technology and enhance their apps with efficient machine learning scripts.

Just like with a previous challenge, a team with strong AI development expertise is a certain way to solve this problem. A strong combination of fintech industry expertise and AI development skills will ensure that ML-powered features perfectly align with the company’s business needs.

Problems with efficient cost management

According to McKinsey’s report, only 50 percent of public fintechs (following their IPO) were profitable in 2022. The same report reveals that the key differentiator between profitable and unprofitable fintech companies was cost management, not revenue growth. This problem is relevant to all areas in which financial businesses engage, including fintech application development.

To manage costs for your AI fintech development project, you need to embrace a relevant cost management framework, plan your budget adequately, and opt for the most accurate and clear finance estimates. In addition, much depends on your software development partner. The team should understand all the underlying aspects of your cost management framework or offer you their own efficient plan for managing your budget. They should also provide you with accurate estimates and work with a strong focus on your key performance metrics (KPIs). All these questions should be clarified and settled before the start of the active software development phase.

Lack of clear growth strategy

Recent studies have revealed companies in the growth stage showed the highest sensitivity to the funding downturn, with a sharp year-over-year funding decline of 50 percent. Growing businesses are vulnerable to downturns because they often fail to establish and adhere to long-term strategies.

To succeed, growing fintech companies should come up with a perfect blend of long-term planning and agility. This principle also applies to implementing AI for fintech. A discovery phase is an ideal solution for a fintech AI project. It will help you get more clarity on the project flow, including its work breakdown structure with estimates, architecture vision, UI/UX design concept, identified risks and risk mitigation strategies, business requirements, and end-user needs.

Users’ resistance to change

The human factor also matters as many teams in fintech are slow to adopt new software and approaches. This problem is especially relevant to financial companies that develop fintech software for internal use. According to McKinsey’s survey, 70% of fintech executives stated that they have faced resistance to change among their employees while implementing AI. Sometimes, such resistance to change makes even the most promising fintech AI use cases fail.

There are several solutions to this problem. You need to explain the tangible benefits of implementing AI into workflows with maximum precision. It is also important to rest assured that your AI-powered fintech software brings real value. Therefore, choose wisely from the list of use cases mentioned above. AI-powered fintech solutions should also be user-friendly and accessible to your employees. That’s why you should pay much attention to implementing an excellent UI/UX design for an AI fintech application.

Conclusions

AI proves to be one of the hottest topics in fintech software development, as more and more fintech businesses are implementing AI for workflow automation, advanced analytics and forecasting, and security. Each of these directions involves a range of use cases that help financial companies become more productive and cost-efficient, enhance customer experience, and protect their data assets from diverse threats.

While AI can completely transform fintech software, there are several challenges that may disrupt fintech AI ideas. These challenges include: the problem of legacy software, lack of expertise, poor cost management, lack of strategic planning, and resistance to change. Fortunately, most of these problems can be solved with thorough preparation and the right team.

Leobit is exactly such a team. We have strong experience in developing AI-powered solutions, while our portfolio includes a variety of successful fintech projects, such as an insurance platform powered with AI scripts. Contact us to discuss your needs and maximize the value of implementing AI for fintech software.

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Artem Matsa | Business Development Director