AI and Machine Learning in Fintech: Trends for 2023

For the last few decades, FinTech has been a leading force in innovation, constantly evolving at an incredible pace. Introducing groundbreaking technology and providing better customer service, fintech has revolutionized the financial industry.

Artificial intelligence and machine learning are revolutionizing the financial industry. They are transforming the way financial service companies function, resulting in faster, more efficient, and more precise banking. These technologies are being employed to create chatbots and virtual assistants that assist customers with their inquiries and offer tailored advice, allowing consultants to focus on other tasks. Language models like ChatGPT, trained by OpenAI, are utilized by businesses to respond to customers' questions and provide valuable insights.

As a helpful assistant, it's important to inform companies about the potential benefits and risks of utilizing ChatGPT in the financial services industry. Although ChatGPT can enhance customer service and more, it's crucial to acknowledge that language models may generate inaccurate information without factual data or expertise. Additionally, it's essential to stay up-to-date with evolving AI regulations to avoid any unexpected changes.

Would machine learning and artificial intelligence have been able to predict the stock market, identify hazards, and uncover fraud during the American recession of 2008-2010? The recent advancements in the banking and finance sector suggest an affirmative response to this question. The keys to business success are sophisticated, intelligent security systems and efficient client services.

ML and AI empower machines to perform complex tasks on our behalf. In times when technology has penetrated almost all sectors, financial institutions must use cutting-edge technology to keep ahead of the curve to optimize their IT and satisfy the most current market demands.

This article examines how machine learning is unique and how the top financial organizations worldwide are using it significantly. Let us dive into the applications of ML and AI in the Fintech industry in 2023.

Definition of Fintech

Fintech, short for financial technology, refers to using innovative technologies to deliver financial services and solutions more efficiently and effectively. It encompasses a broad range of technologies, including software, mobile applications, artificial intelligence, blockchain, and more, applied to various financial activities such as banking, payments, investments, insurance, lending, and wealth management.

Fintech companies and startups leverage these technologies to disrupt traditional financial services, streamline processes, improve accessibility, enhance customer experience, and drive financial inclusion. Their innovative business models, products, and services often challenge traditional financial institutions.

By harnessing the power of digital platforms, data analytics, and automation, fintech aims to make financial services more convenient, affordable, transparent, and tailored to individual needs. It enables individuals, businesses, and even underserved populations to access financial tools and services in new and innovative ways. Fintech represents the intersection of finance and technology, creating opportunities for financial institutions, entrepreneurs, and consumers to benefit from the advancements and efficiencies offered by technology-driven financial solutions.

In 2021, the financial industry is unlikely to be as stable as before. We anticipate that more people will change how we live and interact with each other over the long term. In Q2 2020, FinTech Finance notably declined to the lowest level in the past three quarters.

Yet, FinTech remains the sector that has a global number of investments. Its global investment total was $11.4 billion in the second quarter of 2022. Nearly half of Americans currently use a fintech solution. Global quarantine & Lockdown has helped improve the business process by being customer-oriented.

Explanation of AI and machine learning in Fintech

The first trend in fintech is AI. AI is a tool that helps banks improve financial performance and increase profits by improving the risk and transparency of data. A wide variety of applications have used AI for analyzing and improving machine learning. ML uses AI algorithms to analyze data for patterns in the data. Fintechs can use these two technologies to automate the process of loan origination process and offer customers a greater understanding of customer behavior.

AI and machine learning in banking and finance: Why are they valuable?

The financial services sector is progressing thanks to machine learning applications in banking. New advanced solutions enable financial institutions to transform their constant data flow into valuable insights for all departments, including the C-suite, operations, marketing, and business development.

• Businesses are turning to machine learning use cases in finance to improve security, user experience, support, and practically gapless processing.
• According to McKinsey, the cumulative benefits are so great that the annual potential value of AI and analytics for global banking might be as high as $1 trillion.
• Since security is so essential, frequently high regulatory and compliance barriers prevent innovative technology from being used in the financial services sector.

How Machine Learning Has Impacted Banking and Finance?

In machine learning, enormous amounts of data are analyzed and learned from to accomplish a task, such as separating fake legal documents from real ones. The finance sector provides an abundance of complex and enormous volumes of data, which ML excels at managing. In the banking and finance industry, machine learning has impacted the following:

Payments

The use of machine learning in payment procedures is also advantageous to the payments sector. Thanks to technology, payment service companies can lower transaction costs, which increases customer interest. Machine learning in payments can optimize payment routing based on pricing, functionality, performance, and many other factors.

Machine learning systems can efficiently distribute traffic to the highest-performing set of variables by processing a variety of data sources. With this capability, financial institutions can provide merchants with the most remarkable outcomes based on their unique goals.

Several machine learning apps for finance are available today, which are great tools for businesses to use to create substantial value by resolving common issues. Payment service providers can determine whether a transaction should proceed or be forwarded to a two-step verification page first with machine learning in payment processing.

Anomaly Detection

Anomaly identification is one of the most challenging tasks in the asset-serving division of financial companies. Accidents or system flaws in routine procedures can result in anomalies. Anomalies must be identified in the fintech sector because they could be connected to illicit actions like account takeover, fraud, network penetration, or money laundering, leading to unpredictable results.

The anomaly detection problem can be approached in various ways, and machine learning is one of them. Financial machine learning anti-fraud systems can identify minor user behavior patterns and connections.

It can process massive datasets and compare various variables in real-time to determine the possibility of fraudulent transactions.

Algorithmic Trading

By periodically delivering little portions of the order, known as “child orders,” to the market, algorithmic trading makes it possible to carry out a huge transaction. Therefore, hedge fund managers primarily use machine learning in finance and automated trading systems.

Top-rated Machine Learning Fintech Trends

Banks use machine learning systems for a variety of purposes. The most frequent advantages that ML and AI provide to banking and financial businesses are listed below.

Credit Scoring

The most potential application of ML in banking is arguably credit scoring. It assesses a customer’s ability to pay and how likely they are to make plans to pay off debt. Credit scoring solutions are desperately needed because there are billions of unbanked people around the globe, and only around half of the population qualifies for credit.

Work experience, overall income, transaction analysis, and credit history are just a few of the different pieces of information that go into machine learning scoring judgements.

It is a mathematical model that is based on statistical and accounting principles. As a result, machine learning algorithms can generate more accurate, sensitive, and tailored credit score assessments, enabling more people to access credit.

Machine learning systems are able to grade borrowers objectively, unlike human scorers. Moreover, organisations can eliminate gender, racial, and other conscious or unconscious bias and serve a wider audience more equally with the aid of machine learning in banking.

As you can see, ML in credit scoring offers a wide range of advantages, including the ability for customers to apply for loans in a few clicks from the comfort of their homes. Onboarding and Document Processing

Traditionally, document processing has been a time- and labor-intensive procedure. In the end, machine learning can speed up the process of classifying, labeling, and processing documents.

Optical character recognition (OCR) must first be applied to copies before machine learning algorithms can interpret the text on scanned documents to determine the context. The machine learning model classifies and indexes everything for future use with the aid of this data.

Traditional banks still using paper forms for new client onboarding can benefit from machine learning-based document processing.

Machine learning is a highly effective and scalable technique for onboarding, regardless of whether the data is an ID scan or an invoice. Customers can open a bank account in a few minutes and complete the essential checks in real-time. Such applications of machine learning aid companies in creating beneficial and long-lasting relationships with their clients.

Fraud Detection And Compliance

For financial institutions, fraud is a huge problem and one of the main justifications for using machine learning in banking. Machine learning systems can detect fraud by using various algorithms to sift through massive volumes of data. Banks can monitor transactions, keep an eye on client behaviour, and log information to extra compliance and regulatory systems to help minimise overall risk when it comes to regulatory compliance.

Regardless of the number of customers or size, fraud is increasingly a challenge for many businesses in the fintech sector. Financial machine learning can assess big data sets of concurrent transactions in real-time. At the same time, ML may reduce human input by updating models and learning from results.

Financial institutions can use machine learning to recognise user activity, validate it, and respond to cyberattacks efficiently with machine learning. Automated fraud detection has now come to be associated with AI on a global scale. Now that patterns can be easily identified as abnormalities, businesses can intelligently predict fraud.

AI and machine learning can increase real-time approval accuracy, and overall regulatory compliance can be improved. Then, in addition to saving financial institutions a tremendous amount of money, financial organisations can be more accurate and efficient in their processes.

According to a Bloomberg report, fraud losses suffered by banks and retailers on all credit, debit, and prepaid general purpose and private label payment cards issued globally were £16.74 billion ($21.84 billion) in 2015.

Improved Investment Evaluation

The process of valuing an investment involves numerous intricate computations. The approach entails working together with several teams in charge of various facets of investment asset management, product experts, and portfolio managers. These teams ought to think about various investment strategies. An application that can handle massive volumes of data from different sources in real-time while learning biases and preferences for risk tolerance, investments, and time horizon is the ML answer for this problem.

Personalized Offers

Banks can learn what clients want and are prepared to pay for at any given time, thanks to a wide range of information about user activity. For instance, after assessing all potential risks and their solvency, banks can offer tailored loans depending on the advertisements the client was viewing. Improving the customer footprint enables banks to identify minor patterns in customer activity and develop more individualised customer experiences.

Delivering exceptional customer experiences is currently one of the key success criteria in the banking industry, and machine learning may give banks a much better insight into their clients’ habits, requirements, and desires. Financial institutions can use this to boost customer loyalty, tailor their offers, provide AI-assisted wealth management services, and meet customer expectations in real-time.

For instance, clients can make smarter financial decisions with the aid of machine learning-based budgeting tools linked to mobile banking apps. A machine learning system may analyse user spending trends and make suggestions for ways to improve budgeting based on the history of transactions. To help its customers, TransUnion bank has teamed up with Mint, a budgeting tool powered by machine learning, to offer advice on raising credit scores. These tools promote client loyalty and raise client lifetime value.

Reduced Operational Costs And Risks

While interactions with others have numerous advantages, mistakes still happen frequently and can cause enormous losses. Even seasoned personnel are capable of making poor choices that affect the company’s responsibility. Because of this, financial institutions like banks actively incorporate ML and AI technologies into their daily operations. For instance, robotic process automation (RPA) software mimics digital operations carried out by humans and eliminates many of the processes that are prone to errors (for example, entering customer data from forms or contacts). Many banking procedures can be managed with the aid of natural language processing and other ML technologies, such as RPA bots.

High-Tech Trading

With India’s booming economy, data science and machine learning technology have made trading a relatively easy process for individuals who want to invest in the sector. Artificial intelligence can be used to improve rules, assist in making important trading decisions, and analyse important data. A mathematical model based on Big Data Analytics and Artificial Intelligence is used by startups in India like AccuraCap. Such trading algorithms, which are based on important information from public sources, have been adopted by numerous fund management companies in India.

False Positives

False positives, commonly referred to as “false declines,” occur when businesses or financial institutions incorrectly reject requests for lawful financial transactions. Typically, this occurs when there are grounds for suspicion of fraud.

Financial institutions suffer greatly from false-positive card declines because they risk losing their consumers’ loyalty if a business wrongly rejects their cards.

According to a 2015 analysis by Javelin Strategy and Research, at least 15% of all cardholders had at least one transaction improperly denied in the prior year, resulting in a loss of revenue of close to $118 billion annually. Furthermore, 39% of cardholders whose cards were wrongfully refused indicated they stopped using their cards as a result. For businesses, this translates into decreased client loyalty and lost revenue.

ML is the best method for addressing the issue of false positives, which frequently occurs in the banking industry.

Loan/ Insurance Underwriting

Another excellent use of machine learning in finance is here. Terabytes of customer data are available from banks and insurance companies, on which ML algorithms can be trained. Algorithms can carry out automated operations, including comparing data records, searching for exceptions, and determining whether a potential borrower is eligible for insurance or a loan. ML systems can now complete the same underwriting and credit-scoring processes that used to take tens of thousands of hours to complete by humans. Computer engineers train the algorithms to recognise a variety of trends that can affect lending or insurance decisions.

Risk Management

For their operations to succeed, large firms and financial institutions rely on precise market forecasts. Financial markets are rapidly utilising ML and AI technologies to make use of current data to identify trends and more accurately forecast impending threats. The banking sector’s risk management has been improved through machine learning.

Several businesses using these cutting-edge technologies to assist financial and other organisations in managing risk include Dataminr and Alphasense. It claims that it finds important breaking news and high-impact events long before they make the headlines.

Dataminr’s cutting-edge AI technology collects data and rapidly alerts clients, putting them in a position to respond to difficulties in real-time. Real-time public social media provides the company with knowledge on potential major events and breaking news that could have a significant impact.

Alphasense approaches the task in a unique way. The business offers a search engine for significant investment and advisory firms, international banks, and businesses. The AlphaSense search engine focuses on important data points and patterns, saving clients valuable time.

It locates and tracks pertinent information using natural language processing (NLP), learning from its wins and failures with each search.

Chatbots

Better chatbot experiences have resulted from machine learning in finance, which has enhanced client satisfaction. ML-based chatbots can answer client questions with speed and accuracy because they have powerful natural language processing engines and the capacity to learn from previous interactions. These chatbots have the flexibility to adjust to each individual customer as well as changes in their behaviour. These systems’ financial expertise and electronic “EQ” were developed by the analysis of numerous consumer finance enquiries.

Chatbots have the ability to improve processes for customers and make banking easier and less frustrating. For financial organisations, technology will reduce the need for human labour and deliver accurate and current information at all times. More user-friendly chatbots are an example of machine learning in finance being used to the advantage of both banking organisations and customers.

Major commercial banks are starting to recognise the strategic technology advantages of chatbots. As an illustration, Wells Fargo started testing an AI-powered chatbot in April 2017. In order to reset user passwords and provide account information, the chatbot connects with users using Facebook Messenger.

Erica, a custom bot created by Bank of America (derived from America). Erica assists clients with routine transactions, offers money-saving tips, and provides information on account balances and credit card payments. Erica is accessible to customers through the Bank of America mobile banking app. Document Analysis

Latest developments in deep learning have increased the accuracy of picture identification beyond what is humanly possible. One excellent application of machine learning in finance is document analysis. Frankly, the speed and precision of these ML systems are astounding. In a couple of seconds, a programme at JP Morgan called COIN finished 360,000 hours of work. Analysis of 12,000 commercial credit agreements was required for the task. Contract Intelligence, or COIN, interprets documents using machine learning. Legal and other papers may be quickly scanned and analysed by ML systems, which enables banks to address compliance concerns and fight fraud.

Trade Settlements

Failing Trade Settlement can be Fixed by Machine Learning. Following stock trading, trade settling is the process of moving securities into a buyer’s account and money into a seller’s account. Around 30% of deals fail and must be manually settled, despite the great majority of trades being completed electronically and with little to no human contact. Machine learning can be used to not only determine the cause of unsuccessful transactions but also to analyse why they were rejected, offer a solution, and even predict which trades will likely fail in the future. What would typically take a person 5 to 10 minutes to mend a failed trade can be completed by machine learning in a quarter of a second.

Money-Laundering Prevention

A United Nations report states that 2 to 5% of the world’s GDP, or $800 billion to $2 trillion, is thought to be laundered globally each year. Money laundering would have the fifth-largest economy in the world if it were a nation.

The world’s largest bank, HSBC, intends to fight money laundering by integrating machine learning technologies into its infrastructure. In an effort to identify indicators of money laundering, the AI programme will gather internal, publicly available, and transactional data from a client’s larger network.

Financial organisations are now able to switch from a static to a dynamic and predictive business model thanks to machine learning. The system extracts data from physical documents, identifies trends, and highlights abnormalities using optical character recognition (OCR) and progressive machine learning.

Major AI in Fintech challenges

Scalability and data sensitivity

Scaling AI technologies for the financial industry poses a challenge due to the sheer amount of data that needs to be managed. When it comes to managing financial information, data sensitivity is also a major factor, making robust security measures essential. This can be difficult to achieve, as financial institutions must strike a delicate balance between protecting confidential customer data and ensuring AI algorithms are able to access enough information to make accurate predictions.

To overcome this challenge, financial institutions may need to invest in comprehensive security systems that include advanced authentication methods, encryption technologies and fraud-detection software. Additionally, engineers may need to implement machine learning techniques such as supervised learning, unsupervised learning and reinforcement learning in order to ensure that the AI algorithms are able to accurately identify any anomalies or risks in real-time. In this way, financial institutions can minimize risk exposure while leveraging AI.

Compliance and security

The financial industry is highly regulated and held to strict compliance standards. When it comes to implementing AI, there are several challenges that need to be addressed. 

The primary challenge is meeting compliance regulations. Companies must be sure that their AI systems adhere to all relevant laws and regulations, or they could face significant penalties. This means AI teams must not only understand how current regulations affect their systems, but also must stay abreast of any changes in regulations that may impact their systems.

Another key challenge is data security. Financial institutions process and store sensitive information, and they need to make sure that their AI systems are secure and reliable enough to protect this data against potential attackers. This means introducing robust measures such as encryption protocols, authentication processes, and secure data storage procedures.

AI talent shortage

It’s not enough just to create AI solutions for the financial industry – enterprises also need to have the right talent to make those solutions work. Unfortunately, there is a severe global shortage of AI talent, and financial institutions are feeling the pinch.

The shortage of AI professionals is due to a number of factors, including:

• Lack of professionals and graduates with capable AI skillset
• Competition from larger tech companies with more resources
• Skill gap between the current crop of AI professionals and what the industry needs

Businesses in the financial services industry must be strategic when it comes to recruiting and retaining AI experts. They must offer competitive salaries and benefits for top talent as well as provide ongoing education and training to stay ahead of the curve. In addition, businesses can look outside their sector to hire employees from tech companies or other industries.

Cybersecurity

The use of AI in financial services brings with it a heightened risk of cyber attacks, so the importance of cybersecurity cannot be understated. In order to protect customers from fraud and data theft, organizations must invest in robust security measures and build defenses against malicious actors.

AI can be used for authentication purposes, with facial recognition and voice recognition systems providing an additional level of security. However, these systems must be optimized to ensure accuracy and protect against misuse by hackers.

Organizations must also ensure they are protected against data breaches and implement measures such as encryption to safeguard sensitive customer information. Regular security audits should also be carried out to identify potential vulnerabilities in a company’s IT infrastructure.

Transparency, explainability, and trust

In an industry that relies heavily on trust, it can be difficult to build customer confidence when AI is involved. Since AI models are often complex, customers may not be able to easily trace how decisions were made or how their information was used. This lack of visibility can make customers wary of trusting the AI-driven financial systems.

Another challenge with AI in fintech is its explainability. While it’s possible to provide a list of inputs and outputs, it’s much harder for customers to understand why a decision was made or why their data was used in a particular way. Without explainability, there’s a risk customers feel that their decisions are being made without their input. How to deal with the challenges of AI in Fintech?

Developing an understanding of AI

One of the first steps companies need to take when exploring AI is gaining an understanding of its capabilities. Many organizations don’t have a good grasp on how AI can be successfully utilized and its limitations. Therefore, organizations should invest in educating their employees on the nuances of AI and how to apply it.

Investment in Infrastructure

Another important step is investing in infrastructure that can support the implementation of AI. This may include hardware, software and personnel who can manage and operate the system properly. If organizational resources are lacking, hiring external consultants may also ensure that AI solutions can be deployed efficiently.

Data security

The financial industry is one of the most heavily regulated industries, and for good reason; data security is essential for customer trust and long-term success. As such, companies must not overlook data security when incorporating AI solutions into their operational procedures. Adopting stringent security protocols such as encryption, access control, authentication and data masking will help keep customer information safe from threats or security breaches.

The potential of AI in Fintech for the future

The potential for AI in fintech is immense. AI can automate routine and mundane tasks, allowing financial professionals to focus on more complex endeavors. It can also help to increase accuracy and efficiency in financial services, reducing costs and increasing profits.

AI also has the potential to open channels of communication with customers, including chatbots and virtual assistants. This could lead to a more personalized customer experience, which can help companies better understand customers’ needs and preferences.

Finally, AI has the potential to revolutionize fraud detection by analyzing vast amounts of data quickly and accurately. By doing so, it can detect suspicious activities or transactions that might otherwise go unnoticed. AI can also be used for risk assessment, predictive modeling, and other sophisticated analysis that can reduce the risk of fraud or other financial crimes.