Blog

5 min read

How machine learning and artificial intelligence are used in banking and finance

AI & ML

FinTech

IT outsourcing

Dmitry Nazarevich

CTO

Date: Jan 13, 2023

Upd.: Jan 13, 2023

During the past decade, artificial intelligence has transformed from a concept to a force bringing huge financial gains to businesses across various industry verticals. Neither IT evangelists nor nontechies deny that AI has enormous potential thanks to its disruptive capabilities. Whether it is assisting in designing cloth for fashion brands, outstripping doctors in detecting the earliest signs of cancer, or aiding financial organizations to make informed decisions – AI spans multiple spheres long regarded as distinctly human.As for the AI and ML use cases in banking, Business Insider reports that nearly 80% of FinTech organizations comprehend the benefits of AI for their businesses, whereas 75% of companies with assets exceeding $100 billion are implementing AI strategies in their working routines at this moment. According to another Business Insider report, banks and other financial institutions will save up to $447 billion thanks to AI-based apps.

Applications of AI in banking and finance

We live in a world where AI has become an integral part of our lives, and denying its importance would be shortsighted. FinTech, in turn, offers numerous benefits to stakeholders and customers.

Cybersecurity and fraud detection

Throughout the day, people conduct millions of transactions, including paying bills, depositing money, withdrawing funds, cashing checks, etc. Banks must constantly ramp up their cybersecurity efforts to secure these operations and withstand fraudulent actions in real time before the crime is committed. Banks utilize artificial intelligence to improve digital payments, detect software vulnerabilities, identify suspicious customer behavior and mitigate the risks of scamming. Empowered by ML, AI helps detect and prevent illegal actions like email phishing, credit card/mobile fraud, identity thefts, and fake insurance claims.

For example, Denmark’s Danske Bank updated its obsolete fraud detection software with modern AI algorithms. Due to ML’s capability to compare previous transactions (personal information, data, IP address, location, etc.) and identify suspicious cases, fraud detection increased by 50%, and false positives were reduced by 60%. As banking is a cherished target for all hackers, all-around ML and AI adoption can help financial organizations respond to digital threats and combat cyberattacks before they affect internal systems, employees, or customers.

Chatbots

Using chatbots in banking is one of the finest examples of implementing AI. Once deployed, they stay available around the clock, unlike people with fixed schedules and regular dinner breaks. Moreover, they analyze customer behavior and accumulate proprietary experience, creating user scenarios and behavioral patterns. By integrating AI-enriched chatbots into banking apps, managers can be sure that their clients receive personalized customer support 24/7, with products and services delivered accordingly.

Since 2019, Erica, an AI-powered virtual assistant from the Bank of America, has processed over 50 million customer requests, seamlessly handling tasks such as reducing credit card debt and updating card security.

Loan and credit decisions

Today, banks aim to employ a wide range of smart tools to make loan and credit decisions more informed, precise, and profitable. Conventional banking software is often riddled with errors, inaccuracies in the transaction history, or creditors’ misclassifications. Financial organizations should pay close attention to their credit histories and client references when providing credit resources and assessing an individual or company’s solvency. In short, AI-based systems analyze customers’ behavior patterns to make a data-driven decision about their creditworthiness and send warnings if any controversial or dangerous activities arise.

Tracking market trends

Artificial intelligence in banking help companies manage big volumes of data to elaborate market trends, stocks, and currencies. Additionally, machine learning in banking employs algorithms for measuring market sentiment and suggesting investments. Finance specialists use AI to ensure that stock investments are reasonable and that the risk of failure is low so that they can trade more predictably and profitably.

Customer experience

As time passes, customers expect improved user experience and enhanced convenience while managing banking apps. For instance, the need to visit a bank branch to deposit and withdraw money was eliminated with the advent of ATMs.

Today, people have become more tech-savvy, with banks having to offer new capabilities to process digital payments quickly and securely. Consequently, AI often helps reduce the time needed to record KYC information and eradicate errors. The use of artificial intelligence in banking streamlines fast product time-to-market and mitigates pre-launch hampers. Moreover, customers do not need to go through the hassle of manually applying for a personal loan since AI and ML in FinTech reduce approval times, capturing error-free data about customers’ accounts.

Risk management

As we live in a time of currency fluctuations, political unrest, natural disasters, and armed conflicts, finance and banking are the most affected. During turbulent times, it is important to make prudent investment decisions to stay afloat and avoid financial losses. Here AI comes into play, providing a useful overview of current events and predicting what lies ahead. Also, AI determines whether a client will be able to repay a loan by analyzing behavioral patterns, credit history, and available personal data.

Regulatory compliance

Globally, FinTech is regarded as the most regulated sector of the world economy. As the main legislator, the government monitors and censors banks to prevent them from committing financial crimes, laundering money, or evading taxes.

Legal requirements and standards change frequently, so banks have large departments that research and implement financial legislation. Unfortunately, these meticulous measures require much time and large investments when done manually. Luckily, AI (empowered by deep learning and NLP) subtracts new regulations and evaluates compliance requirements to meet all the external and internal terms and conditions. Even though AI cannot replace a compliance analyst, it can highlight crucial or controversial moments in regulations and protect the company from legislative risks.

Predictive analytics

AI is widely used in natural language analysis and general-purpose semantics. It can detect specific patterns and data correlations that humans or traditional technologies usually miss. Predictive analytics help financial institutions define untapped sales opportunities, data-driven metrics, or industry-specific insights that can drive substantial revenue impact.

Anti-money laundering

As criminals are becoming more sophisticated in their attempts to trick the system, banks should keep an eye on emerging advanced technologies to stay one step ahead of scammers. Obsolete AML systems with outdated rules or thresholds often produce inaccurate results with false-positive alarm rates. AI, in its turn, analyses vast pools of data and raises a red flag if an unusual transaction or suspicious behavior is detected.

For example, the UK’s Financial Conduct Authority (FCA) presented a report on the use of AI in financial services in 2022, concluding that FinTech should “monitor and support the safe adoption of AI in financial services to combat money laundering”.

Process automation

As FinTech requires accuracy, much of the time-consuming or tedious work is delegated to automation. People are prone to errors in light of tiredness or carelessness, which is why robotic process automation (RPA) increases operational efficiency and allows decision-makers to concentrate on core objectives that require human involvement.

For instance, JPMorgan Chase CoiN successfully leverages RPA to review documents and derive crucial data, turning unstructured information into actionable insights.

Why should the banking sector embrace AI?

Today, we observe how banks rapidly shift toward customer-centric relations, implementing a holistic approach to fully meet clients’ requirements and expectations. Customers expect banks to serve them 24/7 at scale, enhancing their journeys with new innovative tools and features. To meet these ambitious expectations, banking organizations should first overcome internal obstacles, such as legacy software systems, data silos, limited budgets, and poor asset quality. Once these obstacles are bypassed, they are half prepared to embrace AI in tackling their everyday problems.

Overall, AI not only ensures unmatched cybersecurity but also makes financial services more convenient and time-saving for both clients and employees.

Challenges in the wider adoption of AI in finance and banking

Despite its undeniable benefits, its widespread adoption is hindered by various issues, such as a lack of credibility and security risks. However, a comprehensive approach to AI and machine learning in finance decreases failure odds and entices significant profits. When embracing AI in finance and banking, decision-makers might encounter problems as follows.

Data security

AI collects, stores, and handles large amounts of sensitive information, which requires decent protection from unauthorized access. Thus, banks should emphasize comprehensive data protection when handling large volumes of AI-related information to eliminate security risks and preserve customer and confidential information secure.

Lack of quality data

Before embracing AI, FinTech companies must structure data to perform tasks correctly. Applying data to real-life situations is impossible if it does not correspond to current realities. Furthermore, data that differs from the machine-readable format can cause unforeseen AI model behavior. Therefore, banks aiming to adopt artificial intelligence should modify their data policies and introduce more order in data flows.

Explainability issues

Since AI-based software weeds out mistakes and save time, they are widely employed in decision-making procedures. Unfortunately, they may have biases derived from previous human judgment errors. The bank’s reputation is at risk when minor discrepancies in AI escalate and cause large-scale problems. Therefore, data involved in AI scenarios should be clear and transparent, leaving no space for controversy and discrepancies.

How Innowise can help with your AI journey

Since its foundation in 2007, Innowise Group has embraced driving-forward technologies that engine businesses and improve our lives through modern technologies. We take full advantage of artificial intelligence, delivering advanced solutions such as voice assistants, NLP-enabled content analysts, customer behavior analysis, fraud detection software, and many more. With our profound support, your business is equipped with tools that ensure the safety of financial assets and immense convenience for both banks and their customers.

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How machine learning and artificial intelligence are used in banking and finance