AI in FinTech: Meet the Top 10 Startups Powered by AI
Have you ever wondered how Fintech startups can provide cutting-edge financial services and products? Two words: Artificial Intelligence.
FinTech and AI go hand in hand to create innovative solutions that drive the financial industry forward. In fact, according to the Cambridge Centre for Alternative Finance, 90% of Fintech companies already use AI.
AI has brought numerous benefits to FinTech, including personalized financial advice, faster fraud detection, increased productivity and improved accuracy. Thanks to AI-powered data entry, Fintech firms have seen an 80% improvement in speed and accuracy. It's no surprise that more than a third of all financial services companies have recently adopted AI.
From automation to customer service improvements, FinTech startups are using AI for various purposes. In fact, McKinsey research shows that 56% of firms use AI in at least one business function. But exactly how is AI used in fintech, and where is the industry heading after taking the most out of it?
Through this article, we will discover how exactly AI alters the face of the financial sector, ChatGPT integration and the most popular AI applications in Fintech.
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Use Cases of Artificial Intelligence FinTech
From data analysis to reducing fraud and enhancing investment strategies, AI has transformed how FinTech startups operate. In this section, we will explore some of the most promising case examples of artificial intelligence in FinTech.
Banks and other financial institutions deal with large amounts of data that must be analyzed and turned into comprehensive reports. This process can be time-consuming, which is where AI comes in.
AI technology allows faster and more accurate data analysis, enabling financial institutions to produce reports quickly and efficiently. By utilizing AI algorithms, companies can identify trends, patterns, and insights that might have been overlooked. This leads to better decision-making, improved risk management, and increased operational efficiency.
One example of AI in financial reporting is using natural language processing (NLP) algorithms to automate extracting relevant information from financial documents. Instead of manually sifting through documents such as annual reports or regulatory filings, AI-powered systems can analyze the text, identify key data points, and generate summarized reports. This not only saves time but also reduces the risk of human error.
With deceptive financial behavior on the rise, from credit card and loan application scams to bogus insurance claims and illegal wire transfers, businesses risk losing millions of dollars.
AI venture funding in the banking industry is often directed towards fraud and cybersecurity, as it is practically impossible to analyze every transaction manually with the vast number of daily financial transactions.
The capability of AI to respond instantly to data, recognize patterns and correlations, and even detect fraudulent activities has proven extremely beneficial. This technology enables fraud analysts to focus on higher-level issues while AI works in the background to detect lower-level ones.
Efficient AI systems can monitor financial transactions in real time, with AI algorithms able to detect abnormalities or strange patterns that deviate from a user's daily spending habits.
With AI-based analysis, investors can quickly identify market trends and shifts that would have otherwise gone unnoticed, allowing them to capitalize on new opportunities in the stock market. AI in FinTech can automate financial processes such as loan underwriting, account opening, and risk assessment.
AI-based algorithmic trading enables the analysis of vast amounts of data in real-time. This provides insights that statistical models cannot uncover, leading the average consumer can now trade stocks and shares using user-friendly mobile apps that employ AI-powered decision-making.
AI in FinTech can quickly recognize and account for anomalies, evaluate financial data to identify trading trends and execute appropriate market trades in real-time. By continuously learning and improving from past data, Artificial Intelligence financial algorithms will help traders to spot profitable trading opportunities and avoid potential losses.
Robotic process automation (RPA)
With RPA, FinTech and banking businesses can automate several typical tasks, including customer onboarding, security checks, trade financing, loan application processes, handling inbound calls for routine queries like account statements and transactions, processing credit cards, account closures, and mortgages.
RPA provides businesses with a cost-effective way to manage back-end office procedures and achieve operational efficiency. By automating routine tasks, RPA ensures accurate and timely processing, reducing the risk of human errors.
Credit scoring and loan prediction
By analyzing past credit histories, income, and other financial information, AI algorithms can accurately predict a borrower's likelihood of defaulting on a loan. This allows lenders to make more informed decisions on whether to approve a loan or not.
Moreover, AI can also help identify new customers more likely to default on their loans. By analyzing various data points, such as demographics, income, credit scores, and social media behavior, AI algorithms can help identify potential defaulters before approving a loan.
When Not to Use AI in Fintech
While artificial intelligence offers tremendous benefits in various areas of FinTech, there are situations where it may not be the most suitable solution. Here are some instances where AI might not be necessary:
- Limited data availability: AI systems require substantial amounts of quality data to generate accurate and reliable predictions. If you're working with limited or insufficient data, the performance of AI algorithms can be compromised. In these situations, it's important to assess whether the data available is sufficient to support AI-driven solutions.
- Cost considerations: Implementing AI solutions can involve significant upfront costs, including infrastructure, data collection, and algorithm development. For certain FinTech startups or small-scale projects, the financial investment required for AI may outweigh the potential benefits.
- Lack of domain expertise: AI applications require domain knowledge and expertise to develop and deploy effectively. If your team lacks the necessary understanding of AI technologies and their implications, implementing and maintaining AI-driven solutions may be challenging. In such cases, partnering with experts or investing in training and upskilling can help bridge the knowledge gap.
- Low complexity and repetitive tasks: If the task at hand is relatively simple, repetitive, and doesn't require complex decision-making or analysis, AI may not be necessary. In these cases, traditional rule-based systems or automation tools can provide efficient solutions without AI algorithms.
- Short-term or temporary projects: The time and resources required to develop and integrate AI systems may not be justifiable for short-term projects or initiatives with a limited lifespan. In such cases, focusing on simpler and quicker solutions that meet the project's specific requirements may be more practical.
Top 10 FinTech Startups with integrated AI
We prepared real-time examples of FinTech companies that have already implemented AI technologies to reinforce the abovementioned words. Let's get going!
ZestFinance helps financial service providers carry out better risk profiling and credit modeling. By leveraging machine learning, the company enables companies to increase approval rates, cut credit losses, and improve underwriting processes.
One of ZestFinance's primary goals is to grant everyone fair and transparent credit access and build an equitable financial system. The company has developed the Zest Automated Machine Learning (ZAML) platform, an AI-powered underwriting system that assists businesses in assessing borrowers with little to no credit information or history. Also, it uses machine learning to analyze hundreds of data points, including traditional and non-traditional data, to predict credit risk accurately.
Enova is a lending platform to provide advanced financial analytics and credit assessment. It has created the Colossus platform, which uses artificial intelligence and machine learning to provide advanced analytics and technology to non-prime consumers, enterprises, and banks to support responsible lending. With this platform, Enova aims to provide credit to those underserved by traditional banks and financial institutions.
Using AI and machine learning, Enova can analyze a borrower's creditworthiness based on various data points, including their credit history, income, and employment status. Enova can quickly and efficiently process loan applications, reducing the time it takes to approve a loan and get funds to the borrower.
Affirm is a consumer application that offers loans for purchases at various retailers, but what sets it apart is its use of multiple machine-learning algorithms for credit underwriting. By analyzing large amounts of data, Affirm provides credit decisions quickly and accurately, which is especially important for online transactions where time is of the essence.
In addition, Affirm has recently become the exclusive buy now, pay later partner for Amazon, allowing customers to split their purchases into manageable payments over time. But that's not all - Affirm also offers a range of other features that improve the customer experience, such as transparent pricing, no hidden fees, and a mobile app.
SESAMm specializes in big data and artificial intelligence for investment, providing organizations with the ability to make timely decisions by tracking ESG, risk controversies, and positive events. One of SESAMm's standout features is its off-the-shelf platform, TextReveal®, which empowers users to generate AI insights from web data on millions of companies in less than a minute.
In addition to providing sentiment analysis algorithms that rely on deep-learning techniques, SESAMm leverages advanced NER (named entity recognition) and disambiguation techniques to properly detect relevant companies in multiple languages.
Bud Financial has developed an open banking platform that allows users to access data from any financial product. The company puts data security above all else and uses advanced encryption techniques to protect users' information. Additionally, Bud is built for privacy and only uses anonymized data to train its machine-learning models.
Bud also places a strong emphasis on transparency and accountability. The company believes in responsible innovation of FinTech and banking and ensures that its clients understand how and why it uses data.
Signifyd generates customer risk profiles to eliminate fraud and customer abuse for retailers, leveraging its Commerce Network and combining data from over ten thousand merchants.
One of the key features of Signifyd is its use of big data and machine learning to provide a 100% financial guarantee against fraud and chargebacks on approved orders. Using Artificial Intelligence in financial services, they can identify high-risk transactions and stop fraud in real-time while reducing false declines.
Kasisto has created a conversational AI platform KAI has made significant stride in improving customer experiences in the finance sector. KAI is helping banks to lower contact center traffic by providing customers with self-service options and solutions.
With KAI, customers can access account information, make payments, and perform other financial transactions without human intervention. AI-powered chatbots analyze customer data and provide tailored financial recommendations to help customers make informed decisions.
Vectra is a cybersecurity startup that leverages the power of AI to detect and prevent cyber threats to financial institutions. Its AI-powered cyber-threat detection platform, called Cognito, automates threat detection, reveals hidden attackers targeting financial institutions, accelerates investigations after incidents, and even identify compromised information.
It can identify hidden attackers, especially those targeting financial institutions. This is achieved through its advanced machine-learning algorithms that can detect subtle patterns in network traffic and user behavior. And automated threat detection, which enables financial institutions to detect and respond to threats in real-time without the need for human intervention. This can significantly reduce response times and increase the effectiveness of cybersecurity operations.
Upstart offers more inclusive and fairer lending practices, making it easier for borrowers to access the credit they need to achieve their financial goals. Unlike traditional lenders that only focus on credit score and years of credit, Upstart uses additional data such as schools attended, areas of study, and jobs held in the past for credit profiling.
Key features are more accurate and personalized credit scores, resulting in higher approval rates and lower interest rates for borrowers. This particularly benefits borrowers with a limited credit history or new to the workforce.
HighRadius is a SaaS FinTech startup that uses AI-based autonomous systems to help automate Accounts Receivable and Treasury processes. The company offers cloud-based Autonomous Software for the Office of the CFO and has transformed the order to cash treasury and record-to-report processes.
HighRadius delivers measurable business outcomes such as DSO reduction, working capital optimization, bad-debt reduction, reduced month-close timelines, and improved productivity in under six months, utilizing AI to automate and optimize complex financial processes.
AI in Fintech: Key Benefits for the Industry
Today, we outline the major advantages of integrating AI into FinTech companies. Keep reading to know due to which benefit AI is in strong demand in 2023.
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.
AI has also made financial security stronger by using speech, facial, and fingerprint identification. This additional layer of security makes it harder for hackers to exploit than standard passwords. In the future, AI-powered security solutions may even replace traditional usernames and passwords as technology continues to improve.
Behavior-based investment predictions and decision making
Despite imperfect data quality, FinTech AI can provide insightful predictions on exchange rates, investments, and trends. One such application of AI is behavior-based investment predictions, which detects possible fraud and boosts engagement by providing more tailored solutions based on user preferences and patterns.
FinTech AI's ability to evaluate unstructured data is impressive, allowing it to forecast product appropriateness and adjust marketing strategies based on consumer behavior. Several smartphone apps with AI support now analyze historical and current data on businesses and stocks to assist investors in making informed investment decisions.
AI investigates every aspect of the data, gathering documents, creating reports, and making predictions to provide a powerful tool for developing actionable business strategies. Machine learning algorithms can identify patterns and trends humans might overlook, allowing for more accurate predictions and decision-making.
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.
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.
Manual procedures are often time-consuming and expensive due to labor expenditures. However, AI can handle these activities and duties, saving time and money.
According to a McKinsey report, 44% of businesses adopt AI technology to lower company costs. With AI, businesses can automate routine customer needs and offer 24/7 support without hiring multiple people.
Additionally, AI can provide tailored insights, prevent money laundering, and conduct credit underwriting in the back office, leading to further cost savings in all three areas of a bank's operations. Banks can offer better deals and attract more clients by reducing costs, increasing their revenue and profit. AI also reduces the chance of human error, resulting in long-term savings on user support spending.
Improved customer experience
To facilitate transactions and answer questions, financial institutions must be accessible around-the-clock, every day of the week. Recent reports indicate that 62% of consumers use AI to enhance their user experience. AI is already making considerable strides in customer support for banks and FinTech businesses, with customers growing accustomed to receiving prompt replies at any time of day.
The inherent learning ability of AI systems means they will only get better at reading customer data and providing personalized experiences. AI models can even evaluate voice and speech traits to produce useful information and predict a customer's mood, directing agents to offer solutions accordingly.
- AI-powered chatbots can provide instant answers to questions, and machine learning algorithms can provide personalized investment recommendations.
- Call centers can benefit greatly from AI-powered chatbots, virtual assistants, and other interfaces that can reduce the workload by addressing common user queries and issues.
- Advanced sentiment analysis can also be used to assess the customer's experience and identify gaps, which can then be used to train chatbots to provide better service.
Better analytics and predictions
According to the O'Reilly report, approximately 48% of companies are using AI to address data quality challenges and enhance analytics. Regardless of the data's quality or size, AI's ability to analyze it is unparalleled. With AI, finance professionals can gain accurate insights and predictions about strategies, investments, trends, exchange rates, and more.
AI systems can analyze data from hundreds of sources and predict what will work and what won't. Additionally, AI can conduct in-depth customer data analyses and predict consumer preferences, product development, and distribution methods. Human analysts cannot compete with the effectiveness of well-trained algorithms.
AI for FinTech with ChatGPT
In addition to the aforementioned benefits, another notable advancement in AI technology that has significantly impacted the FinTech industry is the integration of ChatGPT, a cutting-edge language model developed by OpenAI.
By leveraging ChatGPT and mastering FinTech trends, FinTech firms can
- Provide their customers with intelligent and personalized support through virtual assistants, chatbots, or virtual advisors. These AI-powered conversational agents, fueled by ChatGPT's natural language processing capabilities, offer human-like conversations and tailored assistance to address customer queries and guide them through various financial processes.
- Deliver a seamless customer experience with instant responses and accurate information available anytime. Customers can engage in intuitive conversations, receiving personalized recommendations based on their financial goals and preferences. This level of interactivity and responsiveness enhances customer satisfaction and builds trust in the FinTech brand.
- Handle a high volume of customer inquiries simultaneously. This frees up the human workforce to focus on more specialized tasks requiring expertise. As a result, Fintech companies can optimize their operational efficiency, improve resource allocation, and ensure prompt customer support.
Integrate AI in FinTech with TechMagic
TechMagic specializes in integrating AI into FinTech solutions. We offer a one-stop-solution FinTech app development services, including discovery phase, UI/UX design, engineering, and support, and ensure that all software is compliant and follows domestic and foreign legal FinTech standards.
Our developers bring technical and commercial knowledge to each project thanks to their extensive background in AI. With expertise in FinTech and AI, TechMagic is a valuable partner for businesses looking to be at the forefront of the evolving financial services industry.
FinTech companies worldwide are utilizing various AI capabilities to increase the efficiency and safety of their operations. One key objective of these solutions is to boost productivity, enabling staff to work more efficiently using chatbots and automation tools for data analysis.
The amount of money financial services firms invest in training their employees will determine how much AI is employed in the business. Those who can better utilize new technologies and their capabilities can establish a competitive environment autonomously, form fundamentally new values, and allocate resources more efficiently. However, this may also lead to the financial industry shifting, potentially undermining existing methods.
However, despite the benefits of AI, there is a growing need for more skilled AI developers. As a result, banks are turning to full-cycle software development companies with experienced engineers. If you're looking for an AI development team that works in the FinTech industry, contact us, and we'll assist you in maximizing these cutting-edge technologies.
How to use AI in FinTech?
One key area where AI is being used is in customer service through the implementation of chatbots and virtual assistants. Another area is fraud detection, where machine learning algorithms can identify suspicious transactions and prevent fraudulent activities. AI can also be used to automate financial processes such as underwriting, loan approvals, and risk management.
How will AI help FinTech companies?
By automating various tasks and processes, AI FinTech companies can save time and money while providing their customers with faster and more accurate services. AI-powered solutions can also assist in risk management and fraud detection, which can help FinTech firms provide customers with secure and reliable financial services.
What kind of AI software is used in FinTech?
AI FinTech startups use a variety of AI-powered software solutions, including chatbots, virtual assistants, machine learning algorithms, and natural language processing (NLP) tools. These tools can help automate customer service, fraud detection, underwriting, and loan approvals, among other financial processes.