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July 4, 2022

AI in the Fintech Industry: How Both Companies and Clients Benefit 

Topical and industry-specific, this article couldn't be more timely. AI algorithms are getting smarter and serving fintech needs more wisely.

Dasha Rizoy

Head of Business Development

Artificial Intelligence (AI) is currently a hot topic. Media coverage and public discussion about AI are almost impossible to avoid. It has so many use cases like self-driving cars, chatbots, predictive analytics, cashier-less checkout, and many more. According to a McKinsey survey, 56% of organizations are using AI in at least one business function.

Fintech companies have been benefiting from technologies such as machine learning (ML), artificial intelligence (AI), neural networks, and big data analytics for at least two decades.

These technologies, with the help of data analysis and predictive analysis, allow them to maintain personal contact with their customers, automate their customer support, enhance fraud detection, and facilitate decision-making. There may even be additional benefits as well. This blog post covers the role of AI in fintech industry, as well as what fintech companies have to gain from implementing AI. 

AI in fintech: market overview

The market for artificial intelligence (AI) in the financial technology (FinTech) industry is booming. It was valued at $12.32 billion in 2023 and is projected to reach a staggering $45.07 billion by 2032, growing at a healthy 15.5% per year. This rapid growth reflects the increasing importance of cutting-edge technology in finance.

 AI offers a treasure trove of benefits for FinTech companies, including:

  • Cost savings: Streamlining processes and automating tasks can significantly reduce expenses.

  • Enhanced efficiency: AI can optimize financial management, enabling faster and more accurate operations.

  • Profitability boost: AI-powered tools can help businesses and individuals make smarter financial decisions, leading to increased earnings.

FinTech companies are embracing AI to improve various aspects of financial services, from fraud detection and risk management to personalized financial advice and automated investment platforms. This integration is revolutionizing the financial industry, making it more efficient, accessible, and secure. Now, let’s together dive deeper into what benefits will companies gain by implementing AI.

AI in fintech stats from Statista
Source: Statista

The benefits of AI in fintech

According to the Cambridge Centre for Alternative Finance and the World Economic Forum, a majority of financial services companies and financial institutions say they've implemented AI technology in business domains like risk management (56%) and revenue generation through new products and processes (52%). So, how does AI implementation benefit both businesses and customers? 

Efficiency and Automation

By eliminating human error and improving efficiency, algorithms driven by AI can quickly and accurately analyze massive volumes of financial data. Decisions are made more quickly and customer service is enhanced as a result. In addition, algorithms driven by AI can swiftly and accurately handle massive volumes of financial data, cutting down on human error and improving overall efficiency. Decisions are made more quickly and customer service is enhanced as a result. Fintech companies may benefit from lower operating expenses thanks to AI's job automation and efficiency enhancements, which in turn can allow for more competitive pricing and higher profit margins.

Customer Experience

AI-driven chatbots and virtual assistants have become game-changers in the financial industry, improving customer engagement and satisfaction through every single aspect of customer engagement from banking applications to chat support and instant decision-making. They provide instant and personalized customer support, handle routine inquiries, offer product recommendations, and assist with account management. AI-powered client service is available 24/7, allowing customers to access support anytime.

AI systems also analyze customer data to understand preferences and behaviors. AI-powered chatbots and virtual assistants enable FinTech companies to provide personalized advice and aid customers in making crucial financial decisions. This level of customized attention dramatically enhances the client experience and fosters business growth.

Fraud Detection

The rise of digital transactions and online financial services has increased the number of cyber threats and fraud attempts. AI in payments & FinTech promotes cybersecurity and fraud detection efforts. Businesses will save more than $10.4 billion by 2027 due to the use of

AI-powered fraud detection and prevention systems, according to a Juniper Research analysis.

AI enhances security procedures to protect financial institutions, secure client data, and increase customer trust. By integrating AI-driven predictive analytics into their fraud detection systems, financial businesses can significantly lower the number of false positives (transactions that are mistakenly flagged as fraudulent).

Financial service providers can better detect and prevent fraud by having more control over the data. AI-powered large language models (LLMs) also process delicate or private information within their infrastructure. It decreases dependency on third-party systems, lowers privacy issues, and guarantees the security of sensitive data.

Personalized Services

By utilizing AI powers, fintech companies can proactively predict customer needs and future financial behavior to provide personalized recommendations and value-driven offerings at the speed of need. This, in turn, can significantly boost customer engagement and drive sales. And lower churn rates, as digitally native consumers like millennials and Gen Z – the primary target of sleek marketing – are less likely to quit when being treated individually.

Data Analysis

Analyzing financial data for different reasons is what data analysis in fintech is all about. This is beneficial for companies and financial institutions as

  1. They gain valuable insights. Data analysis helps businesses learn more about their customers, the market, and potential investment prospects.

  2. They can detect fraud in advance. Data analysis can quickly identify unusual patterns in transactions, helping to spot potential fraud and prevent financial losses.

  3. It helps evaluate credit risks more accurately, leading to better lending decisions and lower default rates.

  4. It assists in managing investments. By evaluating market data, AI systems may optimize investment portfolios to maximize returns while avoiding risk.

Cost Reduction

According to a McKinsey report, 44% of businesses embrace AI to leverage automation and cut down their operational costs. 

Moreover, the role of AI in fintech opens up new horizons in terms of saving money. Using AI automation in fintech, companies can eliminate a vast amount of manual work, streamline many business processes, address talent shortages, root out errors, and make the best use of human effort. Employees, in turn, can refocus their efforts on more value-added tasks that require human input while leaving repetitive, time-consuming tasks to AI.

Use Cases of AI in FinTech

Next, let’s look at the details of specific use cases for AI and how is ai used in fintech.

Algorithmic Trading

You may have seen a movie called “The Wolf of Wall Street.” Do you remember the crowded atmosphere on the market floor? Well, now that algorithmic trading can use large datasets to analyze and identify trends and make trading decisions, there aren’t as many people conducting transactions like that. 

Credit Scoring

Developers can train algorithms to scan users’ historical data (including bank documentation and digital footprint) and determine their credit scoring. Moreover, these algorithms can also give users recommendations on how to improve their credit.

Chatbot Customer Support

AI can significantly boost your cloud-based call center software. No more waiting for an operator in a traditional call center. Financial companies are now able to maintain personal contact with their customers via their devices. AI-powered chatbots automate repetitive tasks such as collecting information from clients and replying to frequently asked questions. This reduces human errors and provides a better customer experience, benefiting companies. According to Juniper Research, the global operational cost savings from using chatbots in banking are expected to reach $7.3 billion by next year.

Here are some examples of AI-powered chatbots implemented by prominent banks.

  • Amy, the chatbot used by British multinational investment bank HSBC, processes critical customer feedback and answers additional questions.

  • Amelia, the chatbot used by a Swedish financial group focuses on the employee experience and assists them with internal IT support.

  • Erica, the virtual assistant used by Bank of America, consults with users and helps them save money.

Risk Management

Risk management is a cornerstone of the financial industry, and AI is rapidly reshaping the landscape. Here are some key use cases:

Fighting FraudStop fraud in real-timeAI analyzes transactions for suspicious patterns, identifying and blocking fraudulent activity immediately
Stay ahead of evolving threatsAI constantly learns and adapts to identify new fraud techniques, ensuring ongoing protection
Minimize losses and protect customersAutomated responses trigger instant blocking of suspicious transactions, preventing financial harm
Smarter LendingExpand access to financial servicesAI goes beyond credit scores, considering alternative data to assess risk and extend opportunities
Offer personalized loan termsAI tailors interest rates and repayment schedules based on individual risk profiles
Continuously monitor riskAI dynamically adjusts loan terms as needed, ensuring responsible lending practices
Streamlined InsuranceAccurately assesses riskAI and analyzes vast datasets for precise underwriting and pricing, ensuring fairness and sustainability
Faster claims processingAI automates document and image analysis, speeding up claim resolution and improving customer experience
Prevent fraud and lossesAI identifies suspicious claim patterns to detect and prevent fraudulent activity

Fraud Prevention

Financial fraud is one of the major concerns in the banking industry. Due to how widespread digital technology has become, companies spend more and more on cybersecurity. According to a report by Deloitte, the average business invests between 6% and 14% of its annual IT budget into cybersecurity. Artificial intelligence and machine learning help financial institutions detect and predict threats and fraudulent users rapidly and effectively. 

The challenges of AI in fintech

While AI is continuing to revolutionize the finance industry, three major risk factors must be understood. 

Data Security and Privacy

The hazards involved with AI analysis increase as the amount of data increases. Cybercriminals target artificial intelligence systems because they often demand large volumes of sensitive data. As a result, the risk of data breaches and illegal access increases whenever businesses work together. Malicious individuals may attempt to manipulate AI models for their benefit or to cause disruptions.

Regulatory Compliance

The absence of clear regulations in AI utilization within the financial sector poses a considerable risk to Fintech companies. Proactive navigation of ethical and legal frameworks becomes imperative. Strategic thinking guides the integration of ethical considerations into AI development, ensuring alignment with potential future regulations and preventing unethical usage.

Interoperability Issues

Interoperability problems are a major roadblock for the FinTech sector, which promises customers frictionless banking experiences. Problems with data privacy and security, inconsistent data formats, rigid legacy infrastructure, and conflicting standards are just a few of the many causes of these issues. Integration of disparate systems with multiple standards and data formats is time-consuming and expensive, which in turn increases consumer prices and impedes innovation and competitiveness due to the lack of interoperability.

Ethical Concerns

Machine learning biases pose a significant risk to Fintech companies' commitment to financial inclusion. To address this, Fintech firms must embrace ethical AI practices. By fostering diversity in training data and conducting comprehensive bias assessments, companies can mitigate the risk of perpetuating discriminatory practices and enhance financial inclusivity.

Risk Mitigation Strategy: Prioritize ethical considerations in AI development, emphasizing fairness and inclusivity. Actively diversify training data to reduce biases and conduct regular audits to identify and rectify potential discriminatory patterns.

Why Choose Yellow for AI and Fintech Development?

Yellow is your go-to solution for every single aspect of AI and fintech development. With the huge expertise in the sector, our team proudly stands by as one of the leaders in the sphere delivering innovative and tailored solutions that meet the complex demands of the financial industry. 


AI has already revolutionized the fintech industry and its products. Implementing AI is not easy and requires careful management of both ethical and technical responsibilities. However, it’s no longer a luxury but instead a must-have for financial products and projects dealing with credit scores, customer communication, and financial trends. The influence of AI will increase in the next year, and use cases will certainly continue to evolve.

🦾 Why is AI important for financial services?

Artificial intelligence (AI) in finance plays a crucial role in providing valuable insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and various other tasks.

🦾 How does AI impact job roles in the financial industry?

AI tools offer precise and data-driven insights, ranging from credit risk assessment to investment portfolio optimization. Financial analysts can now utilize AI algorithms to enhance the quality of their services by providing informed recommendations.

🦾 What ethical considerations should be addressed when implementing AI in FinTech?

AI in finance has sparked numerous ethical concerns, such as algorithmic bias, security risks, privacy violations, and a lack of transparency. Algorithmic bias arises when AI systems adopt and reproduce the biases found in their training data, which can result in unjust decision-making and discriminatory consequences.

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