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How AI Implementation Can Benefit FinTech StartupsBack

How AI Implementation Can Benefit FinTech Startups

Over the past few years, Artificial Intelligence has broken into all spheres of life, not excluding the financial field. The data presented in AI FinTech Market Research shows the rapid growth of AI in the area, predicting it will reach a value of $ 60 billion by 2031, and the CAGR will touch 22% from 2022 to 2031. 

AI-based technologies enable members of financial processes to make fundamental technological discoveries and breakthroughs in security, personalization, transactions, insurance, and many others. Powercode’s FinTech professionals have prepared a deep insight into AI’s significance for financial services, its benefits, and the opportunities it presents to your business. Also, you will find information on what bright and promising startups emerged and how they benefited from AI-based software development in FinTech. 

Artificial Intelligence And FinTech

Financial Technology Penetration AI

Artificial Intelligence (AI) is defined as a set of technologies aimed at enabling computer systems to perform cognitive tasks that humans traditionally perform. In the FinTech industry, artificial intelligence is used in five main areas – investment, market research, credit scoring, client support, and regulatory compliance – for information gathering, data processing, transaction simplification and increasing their level of security, enhancing analytics, and providing virtual support. Given the reduced processing time for queries and convenience in terms of financial operations, AI in the fintech sector has recently seen a notable demand rise. 

As we have already mentioned, AI-based solutions simplify a vast range of financial workflows and processes, increase customer engagement rates, provide unbiased decisions, guarantee in-depth analysis of all available data, reduce hazards, etc. Overall, it enhances quality standards and increases manufacturing. FinTech businesses also note the invaluable contribution of Artificial Intelligence and Machine Learning in the financial sector due to its coherent innovative character.

Ways to use AI in Financial Ecosystem 

Ways to use AI in Financial Ecosystem

Credit rating. Financial institutions have to check a customer’s application carefully. Previously, this could take several days or even weeks. But the rise of AI has automated and greatly simplified this process. Besides, it removed issues of bias and cronyism. Previously, this could take several days or even weeks. But the rise of AI has automated and greatly simplified this process. Now no more than an hour passes from the moment the client applies to the approval or rejection of the application without the need to appear in person and fill out endless paper documents. 

Data analysis. Banks and other financial institutions must analyze a considerable amount of data daily. With the help of this technology, it becomes possible to automatically process and immediately enter data, for example, when opening an account or transferring funds. AI can carry out more than ten automatic data checks and recognize more than 60 necessary details from uploaded documents in a few seconds.  

Chat boxes. Representing an omnichannel communication tool that imitates the activity of a living person, chatbots with artificial intelligence are designed to quickly answer common questions and facilitate understanding the principles of providing and using various financial services. They significantly reduce servicing time and optimize workflows within the company.

Risk assessment profiling. Using the principles of artificial intelligence and machine learning, it became possible to train machines to use and analyze customer data over a certain period. An automatic client classification or profiling from low to high risk occurs based on the information received. We want to highlight separately the opportunity for customers to receive recommendations on maintenance and possible services based on the profiling.

Market trends monitoring and prediction. AI enables financial institutions to create real-time forecasts regarding the demand, securities, and currency movement based on analyzing changes and patterns occurring in indexes. It becomes easier to analyze the situation and decide when it is better to invest and when it is better to wait. Financial systems have been able to learn from experience and thereby predict potential market movements, which was previously only possible with the involvement of human resources.

Robo-advisers. These alternative financial advisors provide significant benefits to companies. They can process vast amounts of data, simplify communication with clients, help open an account in real time and monitor transactions. Regardless of what you use, a mobile or desktop application, they are equally handy and practical.

RegTech. As a way to manage regulatory compliance, Regulatory Technology allows companies to automatically adhere to all standards by verifying the accuracy and legitimacy of their actions. It includes customer identification, regulatory analysis, transaction monitoring, and reporting. RegTech is characterized by a high level of agility, configuration speed, seamless integration, and thorough research.

Top AI benefits for FinTech

Top AI benefits for FinTech

Improved Security Level

The issue of security is one of the most crucial in the area of finance. Fintech institutions have to convince and demonstrate to their clients that nothing threatens their finance and privacy. In this case, AI and its functionality can significantly improve data and financial flow security as well as greatly reduce fraud probabilities. 

AI-powered solutions offer a sophisticated identification system equipped with facial, fingerprint, or voice recognition. Such a feature greatly complicates the process of hacking or intercepting data during input and transmission — the fears associated with simply entering a password fall by the wayside. Besides being connected against an accustomed username and password, AI allows users to securely change a lost or forgotten password in a matter of minutes through its technologies like real-time chatbots, for example.

Turning to the issue of fraud prediction, the financial sector has always been distinguished by a large number of transactions and significant cash flows, which are almost impossible for a living person to track concerning suspicious actions. Practical AI algorithms track monetary operations in real-time and thus can spot inconsistencies or weird tendencies in a customer’s usual behavior. It allows fintech companies to deal with suspicious activities more promptly and precisely. 

Boosted User Experience

Personalizing offers and services is another vital aspect of working with clients’ finances. It has been proved that the more financial establishments know about their customers, the easier and more proficiently they can satisfy their needs. It is human nature to pay more attention to offers close to us and involve our most painful problems. Financial customers are not an exception. 

With its ability to analyze large pools of data, extract essential information, find patterns in behavior, predict the participants’ steps in the process, and build a behavior system, AI applications help create offers that suit individual users of a company’s financial services. Such tailored offers are designed for the current financial situation and therefore are the most attractive and acceptable.

AI-based customer support solutions can also facilitate the customer’s life by allowing them to ease their petty worries without leaving home. 24/7 chatbots and Robo-advisors fulfill various service requests, including information transfer, complaints management, and customer support. Automated financial assistants offer personalized recommendations, provide real-time support, and navigate users in their attempts to organize and plan their savings and spending. 

Increased Productivity 

Based on algorithmic principles, AI is designed to reduce the number of repetitive tasks through a simple automation process. The statistics show that almost half of the routine paperwork done during the day can be comptuterised with AI solutions. For example, a machine can easily send “read” notifications or thank-you messages.

In addition, Artificial Intelligence technology makes decisions based on previously collected data and their patterns, which allows it to build a specific set of rules. Such an approach, when programmed precisely, is beneficial to reduce and, in the ideal case, level the number of human errors at work. In terms of equipment, it can monitor their current conditions and predict further actions. So, they can demand spare parts or essential elements for the operation that might get out of order several days or a week in advance, saving time and not stopping the whole process. The ability to automatically configure machine input variables and determine the best machine learning methods will also save time and boost productivity.

Reliable Data Automation

The issue of automating infrastructure tasks, searching and synchronizing data, both about customers and transactions, becomes vital for financial institutions. To approve or reject a loan application, for example, it is necessary to investigate the applicant’s history and financial capabilities – to conduct the so-called credit scoring. At this stage, the question of reliable data processing automation becomes relevant.

AI-based fintech apps use proven and trustworthy data protection tools when analyzing credit history, work experience, or income figures. This allows specialists to deliver precise, customized credit rating analyses depending on real-time elements and terms.

Intensive Algorithmic Trading

Another typical case of implementing AI is to enhance various trading processes in the financial market. Digital solutions equipped with AI features and elements enable the building of sophisticated algorithms to simplify managing deposits and money flows. Based on the AI’s ability to empower algorithms to adapt to varied trading models, stock brokers can apply them to speed up the process and reliability of buying and selling stocks. 

In combination with technical analysis, AI self-learning algorithms provide investors, investment institutions, and other participants with several advantages, including increased accuracy, the removal of lag time when generating payments, continuous assessment of market dynamics, etc. For example, one of the algo-trading types, HFT, has gained the niche’s share allowing it to trade considerable quantities of securities automatically at high-speed rates. AI-powered stock trading enables Auquan, the integrated research platform for capital markets with a headquarter in London, to help their clients to predict possible risks and opportunities by using their tools to process and structure raw data. 

Enhanced Investment Evaluation  

Finance demands a cool head, impartiality, and emotionlessness, especially when it comes to investing money. One of the reasons why the investment sector has started using AI is to remove an “imperfect human” component, e.i. weaknesses typical for people. 

The elaborate investment tools with AI and Machine Learning components can offer recommendations on funding and investment to clients that suit them the best and accomplish their risk tasks. Besides, such tools are responsible for search query accuracy, dividing documents under dates and topics, deriving results according to relevance, etc. These possibilities assist in evaluating proposals and suggestions and mastering complex industry terminology and notions. They are able to analyze and integrate more additional variables, remove the issue of territorial boundaries, and thus reduce processing time.

Notable Implementation of AI into FinTech Sector  2022 (Practical Cases)  


The Foundation Date and Place


BigID 2016

New York, 


BigID is one of the leading data intelligence platforms specialized in managing sensitive data across the company. BigID succeeded in providing businesses with the possibility to comprehend and process various information they gain quickly and share during their operations, no matter what kind of data analytics software they want, cloud-based or on-premise. Implementing machine learning provides the system’s compatibility with several data privacy regulations, such as LGPD, HIPAA, NIST, and others. Since 2016, the startup has received many recognitions for its successful performance, from a 2019 World Economic Forum Technology Pioneer to the #1 Fastest Growing Security Company in 2021 and an RSA Innovation Sandbox winner.
EasySend 2016

Tel Aviv,


EasySend represents a no-code platform that brings the AI tool to develop unique, flexible, and highly personalized digital customer journeys. The enterprise-grade solution meets all standards and ensures a high level of security while integrating with various third-party tools, services, and in-house systems. They transform any insurance and financial processes made by hand into automated workflows with assigned permissions and roles with the help of AI functionality. 
Acin 2017 

London, Great Britain  

Acin is the AI-driven risk control platform designed to digitize and assure operational risk controls providing visibility and protection. It enables users to compare and track control design, connect various controls in one network, single out changes to enhance integrity through diagnostic tools, etc. Acin has earned numerous awards from Europe and North America as an innovator in the operational risk control sector. It was recognized in the AIFinTech100 list 2022 and won’s OpRisk Innovation of the Year. 
HAWK:AI 2018



Hawk AI is a SaaS platform built to detect money laundering and other financial crimes more efficiently and conduct investigations for financial institutions by using AI, with the help of which they quickly find anomalies and discrepancies. Their primary focus is real-time monitoring of transactions and consumers by combining the most robust data sets with a cost-effective cloud deployment approach. A self-learning system prepares and offers preferable actions for operators. The comprehensible AI engine enables users to lower significantly false favorable rates, save time on repetitive tasks, and always control decision-making processes. Hawk AI was recognized as one of the top 100 AI Fintech companies in the world by Fintech Global and one of the world’s most innovative RegTech companies in the RegTech100 in 2022. 
Investfox 2022



Investfox is an online fintech platform with AI functionality aimed to provide customers with independent expert judgments through a simple, sustainable, and viable scoring system that rates investment organizations based on several factors, such as service quality, the speed of responses to customers, product variety, etc. They position themselves as an assistant in the process of choosing what investment company to select and what opportunities to take through the pool of fair reviews. Besides, they are ready to teach their customers to become reliable and self-assured investors. 

Wrapping Up

Artificial intelligence is rapidly changing the business scenery of the financial sector and transforming the entire financial system by modifying the algorithms for transmitting and processing data flows. Not only financial institutions but also individuals are striving to properly and reliably manage their finances, increasing the demand for software products to manage personal finances. Complex digital products based on AI, machine learning, and virtual reality for working with clients and optimizing and protecting companies’ internal processes are emerging, and they will continue to appear on the market. As our material shows, the fintech list of AI companies is rather extensive, and the benefits of using AI are significant and exceed the possible challenges. Please, get in touch with us if you are interested in a high-quality AI-based FinTech solution and want to learn more or are ready to start implementing your ideas.