Artificial Intelligence in FinTech Meet the Top 10 Startups Powered by Artificial Intelligence.

Have you ever wondered how fintech startups can provide advanced financial services and products? The answer to this question can be summed up in two words: artificial intelligence. Fintech and artificial intelligence go hand in hand to create innovative solutions that move the financial industry forward. According to the Cambridge Center for Alternative Finance, 90% of fintech companies use artificial intelligence.
The application of artificial intelligence in fintech has brought numerous benefits to the field, including personalized financial advice, faster fraud detection, increased productivity and improved accuracy. Thanks to AI-based data entry, fintech companies have seen an 80% improvement in the speed and accuracy of their operations. Not surprisingly, more than a third of all financial services firms have recently adopted AI.

From automation to improving customer service, fintech startups are using AI for a variety of purposes. In fact, McKinsey research shows that 56 percent of companies use AI in at least one business function. But how exactly is AI being used in fintech, and where will the industry go after it’s most exploited?

In this article from AI company Avir, we will find out how AI is going to change the financial sector and integrate ChatGPT AI and the most popular AI applications in fintech with this sector. Stay with us.

Use cases of artificial intelligence in fintech
From analyzing data to reducing fraud and enhancing investment strategies, AI has revolutionized the way fintech startups operate. In this section, we examine some of the best case studies of the use of artificial intelligence in fintech.

financial report
Banks and other financial institutions deal with large volumes of data that need to be analyzed and turned into comprehensive reports. This process can be time-consuming; This is where artificial intelligence comes in and becomes important.

Artificial intelligence technology enables faster and more accurate data analysis, enabling financial institutions to generate reports quickly and efficiently. Using artificial intelligence algorithms, companies can identify trends, patterns and financial statistics that may have been overlooked. This leads to better decision making, improved risk management and increased operational efficiency.

One example of artificial intelligence in financial reporting is the use of natural language processing (NLP) algorithms to automatically extract relevant information from financial documents. Instead of manually reviewing documents such as annual reports or regulatory filings, AI-based systems can analyze text, identify key data points, and generate summarized reports. This not only saves time, but also reduces the risk of human error.

Detection of fraud and fraud
With the rise of deceptive financial behavior, from credit card fraud and loan applications to fraudulent insurance claims and illegal wire transfers, many businesses are at risk of being defrauded and losing millions of dollars.

AI venture capital in the banking industry is typically directed towards fraud and cyber security, as manual analysis of each transaction is virtually impossible with the large number of financial transactions occurring daily.

The ability of artificial intelligence to instantly respond to data, recognize patterns and correlations, and even detect fraudulent activity is very beneficial. This technology allows fraud and fraud analysts to focus on higher-level issues, while AI works in the background to identify lower-level issues.

Efficient AI systems can monitor financial transactions in real-time, and AI algorithms are able to detect anomalies or strange patterns derived from the user’s daily spending patterns.

data analysis
With AI-powered analytics, investors can quickly identify trends and market changes that would otherwise go unnoticed, allowing them to capitalize on new opportunities in the stock market. Artificial intelligence in fintech can automate financial processes such as loan underwriting, account opening and risk assessment.

Algorithmic trading
Algorithmic trading based on artificial intelligence enables the analysis of large volumes of data in real time. It provides insights that statistical models cannot uncover, so the average consumer can now trade stocks of their choice using user-friendly mobile apps that use AI-based decision making.

Artificial intelligence in fintech can quickly detect anomalies, evaluate financial data to identify trading trends, and execute market transactions appropriately in real time. By continuously learning and improving from past data, AI financial algorithms help traders identify profitable trading opportunities and avoid potential losses.

Robotic Process Automation (RPA)
With RPA, fintech and banking businesses can perform several routine tasks, including customer onboarding, security checks, trade financing, loan application processes, handling inbound calls for routine requests such as billing and transactions, credit card processing, account closings, and mortgages. automate

Robotic process automation is a cost-effective way for businesses to manage back office procedures and achieve operational efficiencies. By automating routine tasks, RPA ensures accurate and timely processing and reduces the risk of human error.

Credit scoring and loan forecasting
By analyzing past credit history, income and other financial information, AI algorithms can accurately predict a borrower’s likelihood of default. This allows lenders to make more informed decisions about whether or not to approve a loan.

Additionally, AI can help identify new customers who are 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 borrowers before approving a loan
When should you not use artificial intelligence in fintech?
While AI offers tremendous benefits in various areas of fintech, there are situations where it may not be the most appropriate solution. Here are some cases where fintech doesn’t need AI:

Limited data availability: AI systems require significant amounts of quality data to make accurate and reliable predictions. If you are working with limited or insufficient data, the performance of AI algorithms may be inaccurate and compromised. In this situation, it becomes important to assess whether the available data is sufficient to support AI-based solutions.
Cost considerations: Implementing AI solutions can involve significant upfront costs, including infrastructure, data collection, and algorithm development. For some fintech startups or small projects, the financial investment required for AI may outweigh the potential benefits.
Lack of domain expertise: AI applications require domain knowledge and expertise for effective development and deployment. If your team does not have the necessary understanding of AI technologies and their implications, implementing and maintaining AI-based solutions can be challenging. In such cases, partnering with experts or investing in training and upskilling can help fill the knowledge gap.
Repetitive and low-complexity tasks: If the task is relatively simple and repetitive and does not require complex decision-making or analysis, AI may not be needed. In these cases, traditional rule-based systems or automation tools can provide efficient solutions without artificial intelligence algorithms.
Short-term or temporary projects: The time and resources required to develop and integrate AI systems may not be justified for short-term projects with a limited lifespan. In such cases, it is more practical to focus on simpler and faster solutions that meet the specific needs of the project.
Top 10 fintech startups with integrated artificial intelligence
In the following, we have introduced 10 examples of fintech companies that have already implemented artificial intelligence technologies, prepared to reinforce the words mentioned above. Let’s go!

1.ZestFinance

Startup ZestFinance helps financial service providers do better risk profiling and credit modeling. Using machine learning, the company enables companies to increase approval rates, reduce credit risk and improve underwriting processes.

One of the primary goals of ZestFinance is to provide access to fair and transparent credit and create a fair financial system. The company has developed the Zest Automated Machine Learning (ZAML) platform, an AI-based underwriting system that helps businesses evaluate borrowers with poor credit or history. It also uses machine learning to analyze hundreds of data points, including traditional and non-traditional data, to accurately predict credit risk.

Enova

Startup Enova is a lending platform for providing advanced financial analysis and credit evaluation. The startup has created a platform called Colossus that uses artificial intelligence and machine learning to provide advanced technology and analytics to consumers, corporates and non-prime banks to support responsible lending. Using this platform, Enova aims to provide credit to people who are underserved by traditional banks and financial institutions.

Using artificial intelligence and machine learning, Enova can analyze a borrower’s credit based on a variety of data, including their credit history, income and employment status. Enova can process loan applications quickly and efficiently, reducing the time required for loan approval and disbursement to borrowers.

3. Affirm

Affirm is a consumer app that offers loans for purchases from various retailers; But what sets it apart from other competitors is the use of multiple machine learning algorithms for credit underwriting. By analyzing large amounts of data, Affirm makes credit decisions quickly and accurately; This feature is very important for online transactions where time is important.

In addition, Affirm recently became Amazon’s exclusive installment shopping partner, 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 enhance the customer experience, such as transparent pricing, no hidden fees, and a mobile app.

4. SESAMm

SESAMm specializes in big data and artificial intelligence for investment, empowering organizations to make timely decisions by tracking ESG indicators, risk controversies and positive events. A highlight of SESAMm is its ready-to-use platform, TextReveal®, which enables users to generate AI insights from millions of companies’ web data in less than a minute.

In addition to providing sentiment analysis algorithms that rely on deep learning techniques, SESAMm uses advanced NER (Named Entity Recognition) and disambiguation techniques to correctly identify related entities in multiple languages.

5. Bud Financial

Bud Financial has created an open banking platform that allows users to access data for any financial product. The company considers data security more important than anything else and uses advanced encryption techniques to protect user 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 fintech and banking innovation and ensures that its customers understand how and why data is being used.

6. Signified

Signifyd creates customer risk profiles for retailers to prevent fraud and customer abuse; This startup uses its merchant network and combines data from over ten thousand merchants.

One of the key features of Signifyd is the use of big data and machine learning to provide 100% financial guarantee against fraud and refunds on confirmed orders. By using AI in financial services, users can identify high-risk transactions and stop fraud instantly, while minimizing false positives.

7. Kasisto

Kasisto has created a conversational AI platform called KAI that has made significant strides in improving customer experiences in the financial sector. KAI helps customers reduce call center traffic by providing self-service options and solutions.

With KAI, customers can access account information, make payments and complete other financial transactions without human intervention. AI-powered chatbots analyze customer data and provide tailored financial recommendations to help customers make informed decisions.

8. Vectra AI

Vectra is a cybersecurity startup that uses the power of artificial intelligence to identify and prevent cyber threats to financial institutions. Cognito’s AI-powered cyber threat detection platform automates threat detection, accelerates detection of stealthy attackers targeting financial institutions, speeds post-incident investigations, and even identifies compromised data. .

The platform can detect stealthy attackers, especially those targeting financial institutions. This is achieved through Vectra’s advanced machine learning algorithms that recognize subtle patterns in network traffic and user behavior.

9. Upstart

Upstart offers more inclusive and fair 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 the borrower attended, study areas, and past jobs to create a credit profile.

Key features are more accurate and personalized credit scores, resulting in higher approval rates and lower interest rates for borrowers. This is especially helpful for borrowers with limited credit history or new to the workforce.

10. HighRadius

HighRadius is a SaaS (Software as a Service) fintech startup that uses autonomous AI-based systems to help automate accounts receivable and treasury processes. The company offers standalone cloud-based software for the CFO’s office, transforming order to cash treasury and record-to-report processes.

HighRadius delivers measurable business results such as DSO reduction, working capital optimization, debt reduction, month-to-month closing timeline reduction, and productivity improvement in less than six months, using artificial intelligence to automate and optimize complex financial processes.

Benefits of Artificial Intelligence in FinTech: Key Importance of AI for FinTech
In the following, we outline the main benefits of integrating artificial intelligence in fintech companies so that you can understand the high demand for artificial intelligence in 2023.

Benefits of Artificial Intelligence in FinTech: Key Importance of AI for FinTech
In the following, we state the main benefits of integrating artificial intelligence in fintech companies so that you can understand the high demand of artificial intelligence in 2023.

1. Security
   Increased security indicates the use of advanced authentication methods such as speech, face and fingerprint recognition. These methods provide higher security than standard passwords and make it harder for hackers to defraud financial systems.

Artificial intelligence has also strengthened financial security by using speech, face and fingerprint recognition. This extra layer of security makes it more difficult for hackers to exploit than standard passwords. In the future, AI-based security solutions may even replace traditional usernames and passwords as technology continues to evolve.

2. Investment forecasting and behavior-based decision making
   Despite imperfect data quality, AI in fintech can provide clear predictions about exchange rates, investments and trends. One such application of AI is behavior-based investment predictions, which identify potential fraud and increase 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 predict product suitability and adjust marketing strategies based on consumer behavior. Several AI-powered smartphone apps now analyze historical and current data on businesses and stocks to help investors make informed investment decisions.

AI examines all aspects of data, collects documents, generates reports, and makes the necessary predictions to provide a powerful tool for developing actionable business strategies. Machine learning algorithms can identify patterns and trends that humans might overlook, enabling more accurate predictions and decision making.

3. Automatic data processing
   By implementing AI-based solutions, companies can streamline internal processes and reduce repetitive yet error-prone tasks such as invoicing and reconciliation and accounts data analysis. AI solutions can handle mundane tasks like answering common questions, categorizing customers, and monitoring transactions and regulations, freeing up employees’ time to focus on more creative and complex tasks.

This reduces the time and effort required to complete these tasks and the risk of errors that can be costly in the financial industry. Such AI solutions can proactively seek and find opportunities for automation in IT service delivery, improving departmental coordination and agility while maintaining security. This helps improve efficiency and allows companies to handle larger volumes of transactions and data with ease.

4. Saving money
   Manual procedures are usually time-consuming and expensive due to labor costs. However, AI can perform these activities and tasks, saving time and money.

According to a McKinsey report, 44% of businesses use AI technology to reduce company costs. With AI, businesses can automate routine customer needs and provide 24/7 support without hiring multiple people.

In addition, AI can provide relevant insights, prevent money laundering and perform credit underwriting in the back office, leading to greater cost savings in all three areas of bank operations. Banks can offer better deals and attract more customers by reducing costs and increasing revenue and profit. AI also reduces the possibility of human error, thereby saving user support costs in the long run.

5. Improve customer experience
   Financial institutions should be available 24/7 to facilitate transactions and answer questions. Recent reports show that 62% of consumers use artificial intelligence to improve their user experience. AI has already made significant strides in customer support for banks and fintech businesses, and customers have become accustomed to receiving quick responses at any time of the day.

The inherent learning ability of AI systems means they will only get better at reading customer data and delivering personalized experiences. AI models can even evaluate voice and speech characteristics to generate useful information and predict customer mood and guide agents to provide solutions accordingly.

AI-powered chatbots can provide instant answers to questions, and machine learning algorithms can provide personalized investment recommendations.
Call centers can benefit from AI-powered chatbots, virtual assistants, and other interfaces that can reduce workload by addressing common user questions and problems.
Advanced sentiment analysis can also be used to evaluate the customer experience and identify gaps, which can then be used to train chatbots to provide better service.

Artificial intelligence in fintech with ChatGPT integration
In addition to the aforementioned benefits, another notable advancement in AI technology that has significantly impacted the fintech industry is the integration of ChatGPT, an advanced language model developed by OpenAI.

By using ChatGPT and mastering fintech trends, fintech companies can:

Provide intelligent and personalized support to your customers through virtual assistants, chatbots or virtual advisors. These artificial intelligence-based conversation agents, with ChatGPT’s natural language processing capabilities, provide human-like conversations and tailored assistance to address customer questions and guide them through various financial processes.

Provide optimal customer experience with immediate responses and accurate information at any time. Customers can engage in intuitive conversations and receive personalized recommendations based on their financial goals and preferences. This level of interaction and responsiveness increases customer satisfaction and builds trust in the fintech brand.
Handle a high volume of customer inquiries at the same time. This frees up manpower time to focus on more specialized tasks. As a result, fintech companies can optimize their operational efficiency, improve resource allocation and ensure fast customer support.

Artificial intelligence in fintech with Avir
At Avir AI, we specialize in integrating AI into fintech solutions. We provide fintech application development services with an integrated solution and ensure that all software complies with domestic and foreign fintech legal standards.

Our developers bring technical and business knowledge to every project thanks to their extensive background in AI.

With expertise in fintech and artificial intelligence, Avir is a valuable partner for businesses seeking to be leaders in the evolving financial services industry.

And in the end that…

Fintech companies around the world are using various AI capabilities to increase the efficiency and safety of their operations. One of the key goals of these solutions is to increase productivity and enable employees to work more efficiently by using chatbots and automation tools to analyze data.

The amount of money that financial services firms invest in training their employees will determine how much AI will be used in the business. Those who make better use of new technologies and their capabilities can independently create a competitive environment, shape fundamentally new values, and allocate resources more efficiently. However, this may change the financial industry and potentially undermine existing practices.

However, despite the benefits of AI, there is a growing need for more skilled AI developers. As a result, banks turn to software product development companies with experienced engineers. If you are looking for an AI development team working in the fintech industry, contact Avir now! We help you maximize these advanced technologies.

Frequently Asked Questions

How to use artificial intelligence in fintech?
One of the key areas where artificial intelligence is used is 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 activity. AI can also be used to automate financial processes such as underwriting, loan approval and risk management.

How does artificial intelligence help fintech companies?
By automating various tasks and processes, AI-powered fintech companies can save time and money while providing faster and more accurate customer service. AI-based solutions can also help manage risk and detect fraud, which allows fintech companies to provide customers with more secure and reliable financial services.

What kind of artificial intelligence software is used in fintech?
AI-powered fintech startups use a variety of AI-based 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 approval, among other financial processes.