To visualize the momentum behind AI, the worldwide AI market recorded at worth $62.35 billion in 2020 is predicted to grow at a CAGR of 40.2% from 2021 to 2028. With this amazing projection, it is not astounding that tech giants like IBM, AWS, Qualcomm, and Google are massively investing into not just AI research and development but testing and auditing of adverse impact as well. Providentially, FinTech is no exception to this growing trend.
In Financial Services, “Algorithms” was the initial form of technology. APEX, in 1986, introduced AI financial technology called “PlanPower” that was commercially applied and used to develop financial plans for those earning 75,000 USD per year. Today, AI has become a vital part of the FinTech space. In financial institutions, AI drives innovation that leads to fast, personalized, and secure services with global reach. According to a report by Deloitte, AI is transforming the physics of financial services. The report claims that AI is weakening the bonds between the modules of incumbent financial institutions and driving the financial leaders to work in AI-powered operating models.
The AI market for FinTech (Financial Technology) was valued at $7.91 billion in 2020 and is expected to hit $26.67 billion by 2026.
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Pandemic Impact on FinTech
For FinTech startups and organizations, AI is a crucial aspect. No doubt, the digital finance sector was already deep-rooted; the onset of pandemic further catalyzed a swift transformation of this sector. During the pandemic, AI & ML have shifted from a curiosity to a priority, especially for financial services. To deal with the increased demand for online banking and other financial services, AI & ML have been the key drivers of sustainability and growth of the FinTech industry. A study by Mordor Intelligence reveals that the financial AI market was worth $7.27 billion in 2019. This is predicted to multiply by five times by 2024.
What AI & ML use cases are pertinent to FinTech?
FinTech companies whether small or large are employing AI and ML, in combination with powerful apps for several purposes. In the financial sector, companies leverage AI to provide valuable insight into huge data gathered from numerous sources. Through this AI implication, banks can overcome the challenges they face while offering daily services like payment processing and loan management, etc.
Here is an overview of a few of the most pervasive use cases of AI technology in the financial sector:
- Digitizing Documents with AI
Financial institutions (FIs) primarily depend on paperwork to carry out multiple operations such as account opening, lending, trade finance, customer onboarding, and claim processing. After digitizing the structured and unstructured documents, AI-enabled OCR (Optical Character Recognition) accelerates the procedures of extraction, analysis, classification, processing, and verification of data. Thereby, not just the time needed for manual procedures is eliminated, but the digitized documents are easy to find. According to a survey report circulated by Xerox, 46% of respondents complained that excessive paperwork leads to the wastage of a considerable amount of their time. Hence document digitization via AI is reducing manual work done by workers through process automation. AI in financial institutions automates tedious tasks and processes providing employees enough time and energy to concentrate on innovation and creativity that can also boost their morale. - Asset Management
Investment funds are already using complex algorithms for simulations and forecasting. Based on these algorithms, the asset and wealth management department is restructuring most of its processes offering advanced services such as “wealth management tools”. Leading FinTech firms have realized this and are applying AI-based solutions into their apps to benefit users from them. Today users can manage their bank statements and reduce the number of intermediaries while making imperative transactions directly from their devices. Hence, wealth management can eliminate unnecessary processes while reducing operational costs. Autonomous research predicts that by 2030, AI would enable the FinTech industry to reduce its operational cost by 22%. The research firm Opimas predicts that by 2025, AI would reduce the number of employees in the financial sector by 10%. They further predict that 40% of this downsizing would occur in operations that are related to wealth management. - Determining Creditworthiness
AI helps banks as well as other financial institutions to find out someone’s creditworthiness and charge them accordingly. Traditionally, financial institutions analyze the applicants’ credit scores and credit bureau data of the applicants that have been held by agencies such as Experian. But, by employing AI, financial institutions can analyze their customer data and draw inferences from there. AI can easily infer relationships from huge portfolios of consumer data. - Spend Reconciliation
Both are time-consuming and labor-intensive tasks for the accounting sector. AI & ML ensure three-way matching automated incoming invoices from suppliers to get them approved.
Beanworks that is an AP (Accounts Payable) automation provider has lately introduced AI-powered data capture functionality called “SmartCapture”. This functionality promises to considerably improve the speed and accuracy of data entry. Beanworks claims that SmartCapture completes AP processes within minutes while ensuring above 99% accuracy. The company also claims that its SmartCoding technology reduces the time spent on data entry by above 80%. Conventional manual AP data entry costs around 12 to 20 USD for a single invoice to process. According to the president and COO of Beanworks, Karin Ben-Jaafar, an AI solution like SmartCapture gets more intelligent with each invoice as it automatically learns to interpret an AP document and coding. This methodology spares time and energy for the accountants to concentrate more on strategic tasks. - Enhanced Customer Experience – whether it’s about response time or personalization, customers want immediate gratification. AI in FinTech provides the leaders those precious minutes to compete for their business while offering personalized products to their customers.
- FinTech companies are employing AI and ML with NLP. The blends of these technologies along with powerful apps have enabled FinTech companies as well as users to personalize their finances. Smart Wallets are among the most powerful products that help users in managing their finances in the most personalized way. Insurance companies can use ML and mathematical models to observe and explore the customer routine such as exercise, diet, working attitude, and medicinal usage. This helps them to offer personalized life, health, and other particular insurance policy.
Let’s have an overview of a couple of examples where AI-driven financial services are enhancing the customer experience:
- AI Chatbots in FinTech
AI-powered chatbots can reduce the workload in call centers as they handle the most frequent and typical user problems. AI enables chatbots to perform complex sentiment analysis to comprehend the customer experience that your services are providing to them. These chatbots employ automated scripts to resolve the complaints and result in more accessible communication between the bank and the customer. Juniper research reveals that AI chatbots are the emerging future of financial customer servicing. They can better manage numerous requests from customers rather than human representatives. The research forecasts that the interaction through banking-related chatbots will grow 3,1505% would save almost 826 million hours for banks in 2023. The research also reveals that by 2023, 79% of chatbot interactions would be done with the help of mobile banking apps. Chatbots also help the banks to multiply their customer network. For example, just two months after introducing AI-powered chatbots, Bank of America got above one million new clients. - AI-driven Customized Banking Apps
Many banking apps are offering customized financial advice to their clients. This AI-driven feature is helping users to track their income and outgoings and attain their financial goals. Bank of America is offering an AI-powered personalized app to its clients to plan their expenses. Moreover, the institution is also employing AI to predict what the chances of default are for the companies requesting loan management.
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- User Behavior Analysis
AI in FinTech can predict the behavior of the users through AI APIs. For instance, if a user puts a single request for the data of his last month’s expenses. With the AI app, you can predict his follow-up request (for instance, last month’s income) and deliver the data in the same response. Hence, an AI-powered app lessens the number of requests as well as the system load. - Improved Decision Making
AI-based data-driven management decisions are not just highly précised and effective but are cost-efficient as well. In an AI-powered process, FinTech officials take help from machines to find out the answers to their queries instead of humans. Hence they make better decisions based on those answers or solutions recommended by machines. - Algorithmic Trading
Algorithmic Trading, introduced in the 1970s has been widely used by leading trade firms and investors to execute stock market trades. It uses pre-defined and rule-based instructions to perform the analysis. Today, about 70-80% of trades are executed algorithmically. However, AI & ML are taking it to the next level. AI implementation is revolutionizing the trading desk by assisting the system to crunch trillions of data points in real-time. AI applications deliver deeper insight into the stock market that a conventional statistical model could not. AI is bringing algorithmic trading to the common people. Today, consumers have access to mobile apps that are user-friendly enabling them to trade in shares and stocks with the help of AI-powered decision making. An AI-driven system has easy and quick adaptability to a changing trading environment such as a pandemic. It can even have a quick account for anomalies because the ML model is not static and continuously learning from its environment. For instance, NLP solutions analyze the financial reports and the industry magazines to identify trends and adapt rapidly to execute suitable trades. - Increased Security
The financial domain has been the most vulnerable sector for cyber intrusions. AI & ML algorithms are developed to address this issue. In the financial sector, AI delivers many solutions to enhance security safeguards. For instance, banks are offering several apps accessed only through fingerprints or facial recognition. These solutions owe a great deal to AI. Users feel more confident about the safety of their assets as with the help of AI and ML, they can trail everything that is even happening behind their backs. These advanced technologies are also helpful in detecting money laundering. AI and ML employ bits and bytes to find out the corruption network.
Many experts claim the time is not far when AI-powered security solutions would replace usernames and passwords. Speech and facial recognition along with other biometric data can add up another security layer that is harder to bypass than conventional passwords. AI can also monitor and determine customers’ typical behavior through his interaction and hence can offer behavioral solutions leading to a revolution in finance. Suppose a customer tries several times in a row to withdraw 10,000 USD from his account in an outside place that is not his typical location. AI-driven ML would identify and inform this unusual activity as potential fraud and block it. - Digital Fraud Detection
Fraud is considered one of the hottest issues the finance industry is facing these days. Some experts predict that digital banking and virtual payments are increasing the fraud level. Javelin claims that the business and the users experienced a loss of 56 billion USD on account of fraud. According to a CNBC report, digital fraudulent activities against financial service providers increased 109% just in the United States of America during four months of 2021. Using AI in the financial services sector can identify theft, and track malicious activity like card payment. Jane Loginova, CEO of Radar Payments believes that FinTech must invest in AI-driven security systems that spot any distrustful activity in real-time and considerably reduce digital attacks. Using OCR and AI together help detect typography discrepancies; hence fraudulent documents are easily identified.
According to her, the best systems to accelerate data analysis and decision-making will be based on ANNs combined with Deep-Learning (DL) models.
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- Lending Paradigm – Just-in-Time
Since the Pandemic hit hard the consumers and the businesses, the demand for financial assistance such as loans, mortgage, etc., has been continued to soar. To kickstart the return to financial wellbeing, businesses are struggling hard to stay afloat. This market trend is leading the financial sector towards an advanced lending paradigm of “just-in-time”. This paradigm is not possible just through manual operations, here comes AI technology!
Laborious work and intensive time to evaluate the loan applications have been the greatest challenge for lenders. Manual underwriting has been an arduous process. But specialized AI applications automate these laborious processes to a great extent. Low-value loans are approved by AI after performing real-time analysis. Moreover, AI helps evaluate larger transactions like mortgage applications.
Hence, AI solutions are assisting credit lenders and banks in making smarter underwriting decisions. Let’s have a look at the companies providing AI-based platforms to assist the financial sector to rethink the underwriting process:
Enova
It is a Chicago-based company that has developed an AI-based Colossus platform, especially for the financial sector. Enova provides advanced analytics and AI technology to financial institutions to ensure responsible lending. The Colossus platform is very helpful in solving real-life problems of the customers such as emergency costs and bank loans etc., and neither puts the lender nor the recipient in an unmanageable situation.
Ocrolus
It is an NYC-based firm, producing intelligent automation software to enable financial institutions to make better lending decisions. The software ensures that individuals, organizations, and businesses are having enough funding to reach their potential. The ML-powered platform analyzes tax documents, invoices, bank statements, mortgages, and many more to help in deciding loan eligibility, credit scoring, and KYC.
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DataRobot
DataRobot firm based in Boston offers ML-driven software for financial institutions. The software helps financial services providers rapidly develop precise predictive models to accelerate the decision-making process around issues such as lending, fraudulent activities, digital wealth management, blockchain, etc. Crest Financial – an alternative lending firm is utilizing this software for precise underwriting decision-making. This software helps the company to predict those customers having a higher probability of default.
Scienaptic Systems AI
An NYC-based firm providing an underwriting platform for banks and credit institutions to ensure improved transparency while cutting losses. Scienaptic’s Ether provides contextual underwriting intelligence by connecting myriad structured and unstructured data and smartly transforming it. Providing its AI-based platform to a credit card company, Scienaptic systems revealed 151 million USD in loss savings within just three weeks.
Can We Trust AI?
With the digital transformation of financial service providers, we will witness an upsurge in AI solutions in the coming years. In modern banking, if some advanced technology is used, it requires a strong regulatory lens. There would be more regulatory solutions for document analysis and other financial services in the future. Despite the huge potential, AI needs auditing as ML might not always be correct. Diversity along with biasedness is a hot topic regarding AI usage. Dr. Benjamin, working on social facets of AI, algorithms, and privacy at Solent University, UK states that the regulatory feature of AI in the financial sector would possibly include comprehensive audits of algorithms and training data to identify and eradicate biasedness. That’s why in financial services, AI creators need to line up audit trails while developing their solutions.
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Final Word!
Traditionally, finance, banking, and insurance companies have always been forefront in adopting the latest technologies, and AI, being the most advanced technology of the era is no exception to that. AI and ML both are potent Hi-tech tools that are having a considerable impact in the FinTech industry. Financial institutions are producing gigantic data on daily basis in the form of texts, documents, audios, videos, and images. This offers a great opportunity for AI to create substantial value across numerous facets of financial services. AI helps financial players gain a competitive edge, and achieve their growth objectives while making them more relevant to their customers. AI can help them make efficient internal processes and decrease operational costs. Moreover, AI helps in fraud detection, insurance, and wealth management. It augments human intelligence by releasing the employees from tedious tasks, supports better decision making, and pervades intelligence in organizational processes while helping financial institutions deliver outstanding customer experience. Not just the financial institutions, customers can also take advantage of AI through better management of their financials.
As reported by “The Economist Intelligence Unit”,
“Banks and Insurance companies expect an 86% increase in AI-related investments into technology by 2025”.
So AI in FinTech is here to stay and this is the right time for financial service providers to get ready for the AI revolution!