AI in FinTech: A Complete Guide for 2024 [Updated]


AI in FinTech: Market Overview

The AI in FinTech Market is anticipated to reach USD 49.43 billion by 2028, rising at a CAGR of 2.91% over the forecast period (2023-2028). The market size is anticipated to be USD 42.83 billion in 2023. People’s interactions with financial services have changed as a result of the COVID-19 pandemic epidemic. Fintech businesses focusing on payments and wealth management have prioritized strengthening their current infrastructure by adding new assets or increasing capacity to handle the strain that larger transaction volumes would put on their systems. Fintech businesses may have found it difficult, but these moves have created a significant need for AI solutions because their revenue depends on transaction volumes. Such elements are anticipated to drive the fintech market’s desire for AI solutions.

Financial institutions were among the first to use relational databases and mainframe computers. They were eagerly anticipating the advancement in processing capacity. By using techniques adapted from more generalized components of human intellect, artificial intelligence (AI) enhances outcomes. Fintech firms have transformed as a result of the recent computational arms race. Thanks to technologies like machine learning, AI, neural networks, big data analytics, and more, computers can now process considerably more extensive, more varied, and deeper information than ever before.

  • Additionally, banks and Fintech have profited from AI and machine learning because they can handle enormous volumes of client data. Results on timely services or items that clients desire are obtained by comparing this data and information, which has fundamentally helped.

Additionally, machine learning is used to build propensity models at previously unheard-of rates. Banks and insurance organizations now offer machine learning-based solutions online and now is the right time to develop a fintech application. Anticipating the consumers’ product propensity based on real-time behavioral data has further improved real-time target marketing. Several market leaders are carving out a position for themselves by directly providing solutions, such as AI Chatbots for banking. For instance, Talisma and Active in June 2021.Ai must use a conversation AI-powered Chatbot to deliver enhanced customer experience in BFSI.

Who Needs AI in Fintech?

The following fields see the most use of AI and ML:

  • Security: Fraud costs banks billions of dollars every year. AI has made it easier for businesses to identify suspicious activities and enhance cybersecurity.
  • Investment: Worldwide, robo-advisors are in charge of managing about $4.6 trillion in assets. Additionally, cutting-edge platforms like Vinovest open up new avenues for investing.
  • Productivity: AI-based software has many uses that enhance data processing or take over administrative duties (like invoicing), and it has a successful track record.
  • Customer support: Chatbots with AI and ML capabilities benefit customers and cut expenses for enterprises.

Top 5 AI Fintech Startups in 2024

To support the lines above, we prepared current instances of FinTech businesses that have already applied AI technology in finance. Let’s move forward!

top 5 ai fintech startups

1. ZestFinance

Financial service companies may do better risk profiling and credit modeling with the aid of ZestFinance. The business helps businesses boost approval rates, reduce credit losses, and enhance underwriting procedures by utilizing machine learning.

Building an equal financial system and granting everyone access to credit are two of ZestFinance’s main objectives. The startup has created the Zest Automated Machine Learning (ZAML) platform, an underwriting tool driven by AI that helps businesses evaluate potential customers with little to no credit history or information. Additionally, machine learning adequately predicts credit risk by analyzing hundreds of data points, both standard and non-traditional.

2. Vectra AI

A cybersecurity firm called Vectra uses artificial intelligence to identify and stop cyberattacks on financial institutions. Its Cognito AI-powered cyber-threat detection tool automates threat detection, exposes covert attackers aiming for financial institutions, quickens incident investigations, and even identifies compromised data.

It can locate covert attackers, particularly those that target financial institutions. This is made possible by its sophisticated machine-learning algorithms, which can find minute patterns in user behavior and network traffic. Additionally, financial institutions may detect risks in real time and take action without the help of humans thanks to automated threat detection. This can speed up cybersecurity activities and considerably cut down on reaction times.

3. Affirm

Affirm is a consumer application that provides loans for purchases made at different shops, but it stands out in how it underwrites credit using a variety of machine-learning algorithms. Affirm rapidly and correctly offers credit judgments by evaluating vast volumes of data, which is crucial for online transactions where efficiency is vital.

Additionally, Amazon’s buy now, pay later program exclusively includes Affirm, allowing customers to make their purchases manageable payments over time. Affirm also provides features like transparent pricing, no additional fees, and a mobile app that enhances the client experience.

4. HighRadius

HighRadius is a SaaS-based FinTech firm that leverages autonomous AI-based tools to help streamline Treasury and Accounts Receivable procedures. The business has modernized the order to cash treasury, record-to-report, and independent software for the office of the CFO operations.

HighRadius uses AI to automate and optimize complex financial processes, resulting in demonstrable business benefits, including lower DSO, working capital optimization, insufficient debt reduction, shortened month-close delays, and increased productivity in under six months.

5. Bud Financial

Bud Financial’s open banking platform users may obtain information from any financial product. The organization prioritizes data security and employs cutting-edge encryption methods to safeguard consumer data. Additionally, Bud was created with privacy in mind and exclusively trains its machine-learning algorithms on anonymized data. Further, accountability and openness are highly valued by Bud. The business upholds ethical innovation in banking and Fintech, and it ensures that its customers know how and why data is used.

List of Top 5 Fintech Companies using (AI) Artificial Intelligence

list of top fintech companies using ai

1. Highen fintech

The foremost supplier of cutting-edge financial solutions is Highen. We use advanced technological solutions to improve the financial services sector while empowering businesses and consumers. At Highen, we understand that every customer has particular needs and objectives.

2. Persefoni

Enterprises and institutional investors may monitor, evaluate, plan, predict, and report on their carbon footprints thanks to Persefoni, a SaaS startup. Utilizing AI, the Persefoni platform enables customers to monitor their carbon transactions and inventories with the same rigor as their financial ones. It provides them with contextual sustainability performance rankings for their firm. Persefoni is an all-inclusive carbon footprint monitoring solution for reporting firms and institutional investors.

3. Tipalti

“Tipalti is the only payables automation system that integrates every aspect of a worldwide accounts payable workflow into a single, all-encompassing cloud platform. Finance departments can easily manage every part of their AP and supplier payments process using Tipalti. Leading businesses utilize Tipalti to save up to 80% of the burden associated with supplier payments, enabling them to expand their operations effectively for international expansion while enhancing financial and tax compliance controls. Roku, Zumba, Twitter, GoDaddy, Zola, Twitch, GoPro, Foursquare, and Vimeo are just a few businesses that trust Tipalti to change supplier payment processes.

4. Orbital Insight

“Orbital Insight is a pioneer in the large-scale analysis of satellite and UAV pictures and works at the interface of big data and the commercialization of space. To give a comprehensive understanding of the world that is statistically based on observation, the business has created algorithms to count and measure vehicles, roads, aircraft, clouds, haze, freshwater lakes, agricultural areas, buildings, and oil tanks. Over 60 asset management companies, several US government agencies, and two international non-profit organizations are among the company’s clients.

5. Onfido

“Founded in 2012 by entrepreneurs from Oxford University, we’re a private global technology firm to disrupt the background checking industry. Our dedicated software engineers, designers, product specialists, and customer service professionals work tirelessly to revolutionize how companies carry out background checks.”

The Benefits of AI in Fintech

Today, we outline the significant advantages of integrating AI into FinTech ai companies. Keep reading to know due to which benefit AI is in strong demand in 2023.

  1. Cost-saving: Artificial intelligence decreases many expenses on customer service, fraud prevention, reallocation of clerical tasks, and more.
  2. Security: Increased security indicates advanced authentication methods such as speech, face, and fingerprint recognition. These methods offer enhanced security compared to standard passwords, making it harder for hackers to exploit financial systems.
  3. Better analytics: Well-trained algorithms are just as effective (or better) than human analysts. However, you can look into an AI’s inner workings and see precisely how a decision was made, which you can’t do with a person.
  4. Improved customer experience: Millennials and younger generations are much more tech-savvy. Using it feels natural to them, so digital services aren’t just a convenience but a must. As repeat business is significant, better UX plays a big part in AI-driven fintech becoming as popular as it is.
  5. Automated data processing: By implementing AI-powered solutions, companies can streamline internal processes and alleviate repetitive yet error-prone tasks, such as invoicing and reconciling accounts data entry and analysis. AI solutions can handle routine tasks such as answering common questions, categorizing clients, and monitoring transactions and regulations, freeing employees to focus on more creative and complex tasks.
  6. Reduce time and effort: This reduces the time and effort required to complete these tasks and the risk of errors, which can be costly in the financial industry. Such AI solutions can actively seek out and find opportunities for automation in IT service delivery, improving departmental coordination and agility while maintaining security. This can help improve efficiency, allowing companies to handle larger volumes of transactions and data easily.
  7. Cost-saving: Manual procedures are often time-consuming and expensive due to labor expenditures. However, AI can handle these activities and duties, saving time and money.

A McKinsey survey claims that 44% of companies use AI technology to cut expenses. Businesses may automate everyday customer demands and provide round-the-clock service using AI without hiring more staff.

Furthermore, AI can handle credit underwriting in the back office, stop money laundering, and give personalized insights, reducing costs across all three facets of a bank’s operations. Banks can provide better bargains and attract more customers by lowering expenses and raising revenue and profit. Additionally, AI reduces the possibility of human error, which results in long-term savings on user support expenses.

The Top 5 Applications of AI in Finance

This is one practical use of artificial intelligence in the fintech industry.

the top 5 applications of ai in finance

1. Wealthfront offers general financial guidance

With 440,000 members and $25 billion in assets under management (AUM), Wealthfront is one of the market leaders in the robo-advisor sector. The minimum account requirement is only $500, offering many investing possibilities, including cryptocurrency. It also has relatively cheap costs, with most accounts paying just 0.25 percent and no transaction fees.

UBS acquired Wealthfront at the beginning of 2022, and it is projected that the company will expand even faster than previously.

2. Digital assistants: Kasisto

Conversational AIs are made by Kasisto so that users may ask inquiries and receive reliable answers. It uses sophisticated natural language processing (NLP) offered in packages specifically designed for consumer banking, commercial banking, and investment management.

Kasisto has acquired several well-known clients, including J.P. Morgan, Emirates NBD, and Westpac, and has garnered $66 million in venture funding.

3. Adyen provides a wide range of goods

Adyen also issues cards as a digital bank, POS app, card issuer, analytics platform, and payment processor. Among this Dutch company’s many uses of AI are risk management, customer insights, and revenue optimization. Among its clients include McDonald’s, Uber, LinkedIn, and Everything is centralized on a single platform for the client’s convenience.

4. Perfios Data analysis

This Indian company offers a robust data analytics platform mainly used by banks and non-bank financial institutions (NBFIs). It helps with asset management, fraud protection, better loan choices, acquiring reliable credit scores, and fraud prevention.

Deutsche Bank, Canara HSBC, and Home Credit Finance are just a few of the companies Perfios has as clients and has received over $120 million in investment.

5. Suplari: intelligent financial management

Suplari aids businesses with budget management and cost-cutting opportunities. Its platform eliminates the requirement for an internal staff of analysts and a deluge of Excel sheets.

Companies using the platform include 21st Century Fox, Nordstrom, and Hulu. Microsoft purchased Suplari in 2021 for an unknown sum.

The Challenges of AI in Fintech

You may encounter a few obstacles if you want to discover how AI is applied in finance.

the challenges of ai in fintech

1. A large amount of delicate data to process

Financial information is equally as private as medical or personal information. Whether in B2B or B2C models, you must deal with much of it if you want to scale. This calls for designing your algorithms to be safe and paying particular attention to hacker-proofing your databases, procedures, and workflows. Not to add, considerable optimization work will be needed to provide a positive user experience when analyzing this.

2. Compliance

The financial sector is heavily regulated. This entails making extra efforts to adhere to applicable state, federal, and potentially international laws. To reduce future regulatory issues, you must ensure that your technological and legal teams work closely together.

3. Creating a Trust

People and organizations are cautious and will only trust people with a good concept with their money. You’ll have to put in much effort to win clients’ trust. Your work gets much more difficult when you factor in some people’s suspicion of AI.

4. Increased minimum

In the financial industry, reputation is vital, and even one high-profile failure may destroy the carefully cultivated reputation of your business. As a result, you must work on both edge instances and the most prevalent ones.


One thing is abundantly evident as we end our revised guide on AI in the Fintech business for 2024: AI is no longer the future; it is present. AI is fundamentally changing the financial sector in various ways, from AI-driven chatbots that offer immediate customer service to sophisticated algorithms that shape investing strategies. Accepting the promise of AI is not simply a choice; it is a must for being competitive and satisfying the changing requirements of consumers as the sector continues to develop and adapt. The continued development of AI will impact the future of Fintech app development company; thus, success in the following years will depend on how well-informed and proactive businesses are about adopting it.

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