AI in Banking and Finance — Series 1
Overview
The financial sector collaborates with every other industry, especially the consumer-centric ones. Its main functions involve safeguarding assets, easing the process of exchange/payments and making loans available to individuals, businesses and government sectors. Being a crucial industry, it is important to dive into the technological advancements and their potential in this industry.
Artificial Intelligence has been adopted by many banking and finance firms, ranging from partial to full adoption. Solutions that can learn about our world by themselves, and then organize and interpret data to make predictions have become an essential part of this industry. With the inception of big data technologies, the availability of high computational resources and the increase in competition in the industry, more businesses are capitalizing on the advantages offered by AI.
Why is Artificial Intelligence Important in Banking and Finance?
Personalization without Branch Offices
Physical bank branches in various geographical locations have made banking services more accessible to people in the vicinity. These offices also make personalization possible, the perceived cultural, social and economic needs of the customers are considered in their financial services recommendations. The inception of online banking has reduced the need for banks to have costly edifices. With the addition of artificial intelligence, wider coverage and personalization are available to customers in the comfort of their homes. This technology enhances customer experience, operational efficiency, resource allocation optimization and broad service reach. AI uses advanced analytics and recommendation models to offer personalized financial products and services.
Faster Delivery of Services
Processes like loan approvals, customer onboarding verification and check deposit processing that took ages with traditional manual approaches take less time with AI. This involves instant payments, real-time fraud detection, AI Chatbots support, real-time compliance checks and market analysis.
Innovation and Disruptions
The finance industry is experiencing a wave of new entrants leveraging AI to disrupt traditional financial services. Fintech startups use AI to innovate rapidly, offering new financial products and services that cater to evolving customer needs. Dr. Lewis Z. Liu, CEO and co-founder of Eigen Technologies says
“Think about how Venmo transformed the mobile payments space, or how Klarna changed the game for short-term financing. It was less about bringing something new to financial services and more about changing the actual way it was done”.
…
AI-Powered Fraud Detection and Prevention
With consistent technology advancements, the number of fraudulent actors increases. This development presents an increased threat level to the banking industry. Sophisticated fraud patterns are emerging to match the banking system's significant security efforts. According to a report by Seon, for every $1 defrauded, $4.23 in the US and $3.78 in Canada are lost by financial institutions through legal payments, investigation fees and recovery expenses. Fraud detection via high-performance machine learning algorithms is key to improving the operations of these financial bodies. These algorithms monitor transactions in real-time to detect unusual patterns that may indicate fraudulent activity. Through user behaviour analytics, AI can personalize security measures, ensuring that each customer’s account is protected according to their risk profile.
Examples of Machine Learning Algorithms for Fraud Detection
- Supervised ML Algorithms
Supervised Machine Learning models learn from labelled data and detect patterns indicative of fraudulent activities. Examples are Logistic Regression, Decision Trees, Random Forests and Support Vector Machines.
Logistic regression is a statistical technique that analyses the relationship between the dependent variable and one or more independent variables (such as transaction amount, frequency and location) to predict the likelihood of a fraudulent transaction.
Decision Trees use tree-like representations of decisions and consequences. A decision tree splits the data into branches based on feature values, leading to a final decision at each leaf node. The model predicts whether a transaction is fraudulent by learning simple decision rules inferred from the data features.
Random forest is an ensemble learning method that combines multiple decision trees to improve accuracy and reduce the risk of overfitting. In fraud detection, the random forest algorithm analyzes large amounts of transactional data and detects patterns that may not be apparent using traditional rule-based systems.
Apart from regression i.e. predicting a likelihood, Support Vector Machines (a.k.a SVMs) are also used for classification where the result is either fraudulent or not, belonging to a group.
2. Neural Networks
Neural networks consist of layers of interconnected neurons that process the input data to learn and predict fraud based on intricate patterns. They are used to handle complex and high-dimensional data, detecting fraudulent patterns. Examples are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks.
3. Clustering Algorithms
Clustering algorithms partition the data into clusters based on similarity, with outliers flagged for further investigation. Similar transactions are grouped to identify outliers that may represent fraudulent activity.
Real-Time Transaction Monitoring
With advanced analytics and machine learning algorithms, financial institutions (FI) create sophisticated models that identify potential fraud in real time. This process involves continuous analysis of transactional data to detect signs of fraud or suspicious activities. Algorithms consider transaction amount, location, customer’s historical behaviour and other important patterns, compare them against a vast database of known fraud patterns and use machine learning to identify new and emerging patterns.
Implementing real-time monitoring qualities to regular fraud platforms powered by machine learning requires low-latency infrastructure. The analytics must be done in milliseconds at a point where reversals and alerts are possible and can still save the situation. Some of the necessary infrastructure for effective real-time fraud detection are systems with fast Application Programming Interface (API) responses, big data technologies to handle large volumes of data, continuous optimization of risk scoring models and automated alert systems.
According to Startups Magazine’s report for the year 2023, banks in the UK spend nearly £219.7 billion each year on tackling financial crime through various efforts (AI inclusive). The infrastructure required to implement AI has cost implications but is necessary compared to the billions of dollars in financial fraud costs. One of the key benefits of real-time transaction monitoring is that it allows for the implementation of customizable risk-scoring models. Transactions with high-risk scores are prioritized for further scrutiny, enabling organizations to focus their resources on potentially fraudulent activities. Real-time transaction monitoring yields faster fraud detection. It also reduces the number of misdiagnosed but legitimate patterns called false positives and improves customer experience.
Predictive Analytics in Fraud Prevention
Predictive Analytics anticipates unknown future events and is important in identifying and preventing potentially fraudulent activities. Deep learning techniques excel at uncovering subtle, often hidden patterns and irregularities in financial transactions, communication channels, and customer behaviours. This capability enables predictive models to detect suspicious activities with remarkable accuracy, identifying potential fraud before it happens.
This is possible through continuous learning and adaptation of models, anomaly detection capabilities, time series analysis, and flexibility of the predictive models. Predictive analytics also achieves better accuracy when models are customized to suit the financial body it caters to. Organizations consider their location, sector, historical patterns, customer base and other factors and behaviours for better predictions. These models are usable by banks, payment firms, investment agencies and other financial agencies.
AI Tools for Identifying and Mitigating Cyber Threats
Some tools detect and prevent cyber threats using AI capabilities. These are a few of them:
1. McAfee Identity Protection Service
According to PYMNTS Intelligence, misuse of account information is the leading type of fraud. 38% of fraudulent transactions are attributed to account identity misuse. McAfee’s service can monitor your accounts, learn from the analyzed patterns and alert you of potential threats when they arise. These threats include data breaches of personally identifiable information (PII) like Social Security Numbers, bank details, email addresses and passwords. Millions of malicious links globally are analyzed to provide better detection.
2. Darktrace
Darktrace is an AI-powered cybersecurity platform that monitors users’ networks and emails for active threats. It uses the Enterprise Immune System, a self-learning AI system to detect and respond to cyber threats. It utilizes machine learning techniques to learn the typical behaviour patterns of an organization’s network and subsequently detects any irregularities or deviations from this established norm.
Vectra’s Cognito IPS platform applies AI to analyze traffic from public cloud sources, Software-as-a-Service (SaaS), user identity information, Networks and EDR to detect and block malicious attacks.
The Role of AI in AML (Anti-Money Laundering) Compliance
Anti-Money Laundering (AML) is a set of regulations that monitor, prevent, detect and report money laundering. Money laundering is the illicit process of disguising the origins of money obtained through criminal activities, such as drug trafficking or terrorist financing, to make it appear as though it comes from legitimate sources. Essentially, it is disguising ‘dirty’ money to appear ‘clean’, hence the term ‘laundry’. Assets earned illegally may be laundered through cash-intensive businesses like marts or restaurants where the illegal cash is mingled with business cash.
Several laws mandate various parts of AML Compliance. Some of them are that financial firms must
- Have internal controls, policies and procedures to combat money laundering
- Have a designated AML officer
- Include customer identification procedures
- Report suspicious activity to the right enforcement agencies.
- Conduct monitoring of customers’ transactions.
Traditional rules-based AML systems use predefined scenarios or “rules” to identify suspicious activity. Transactions are analyzed and flagged if they meet rules indicating money laundering. In recent years, AI has improved AML transaction interceptions. AI systems can detect suspicious transactions in real-time.
AI algorithms analyze huge amounts of data and detect suspicious patterns quickly and accurately. Historical data, customer behaviour and digital footprints are used in machine learning models. These models keep learning and improving in AML detection over time too.
Case Studies: Successful AI Fraud Detection Systems
Many financial institutions have implemented fraud detection ecosystems using Artificial Intelligence. Here is one of them:
Mastercard and its partners (Verizon Business, Entersekt and Global Anti-Scam Alliance) in this pursuit implemented a sophisticated AI-driven fraud detection system to enhance security and protect its customers from fraud. Mastercard Identity verifies users’ information safety and authenticity, ensuring account ownership during every transaction or activity. Mastercard’s behavioural biometrics and Mastercard’s Consumer Fraud Risk solution also monitor physical interactions and financial patterns to help detect and prevent payments to scammers.
So far, Mastercard’s use of AI has yielded positive impacts. According to their reference to one of the users, Trustee Savings Bank (TSB),
After launching in 2023, early indications of Consumer Fraud Risk’s impact are very promising. Last year, TSB estimated the amount of scam payments prevented in the UK over a year would equate to almost £100m should other banks mirror its performance.
Recently Mastercard has included Generative AI as a tool in its fight against Card Fraud. According to them, the result is
reducing false positives during the detection of fraudulent transactions against potentially compromised cards by up to 200%
increasing the speed of identifying merchants at-risk from — or compromised by — fraudsters by 300%.
Future Trends in AI-Based Fraud Prevention
Based on current trends, it is clear that fraud prevention technologies are poised to become more versatile, offering AI-enhanced protection against financial crimes. However, as these technologies evolve, so do the tactics of fraudulent actors. This will lead to the creation of new and complex financial risks. We will explore what such a future could bring.
Advanced Trends in AI-Based Fraud Prevention
- Technological Improvements of Fraudsters
As fraud prevention tactics intensify, fraudsters increasingly utilize AI to enhance their activities. Some of the ways this is done are
a. Deepfake Technology:
Impersonations via mimicked images, cloned voices and audio recordings improve their social engineering attacks, making them more convincing. Writing styles are also learnt by AI models and utilized in automated phishing attacks.
b. Exploiting AI Systems:
Fraudsters can exploit vulnerabilities in AI systems by feeding AI models with manipulated data to deceive fraud detection algorithms. By understanding how AI-based security systems work, fraudsters can develop methods to bypass them, such as generating legitimate-looking fraudulent transactions that slip through detection.
c. AI-Driven Malware
AI-driven malware can learn from its environment and modify its behaviour to avoid being flagged by anti-virus software. Using technologies like LLMs and GPTs, the malware can replicate itself and create unique variants, making signature-based detection infeasible.
d. Behaviour Analysis
Fraudsters use AI to analyze and predict the behaviour of financial systems and individuals, identifying weak points and optimal times to initiate attacks. AI models can analyze stolen credit card data to predict which cards are most likely to have high balances or be less likely to trigger immediate alerts.
e. Social Engineering
AI chatbots can engage with potential victims in real-time, responding intelligently to queries and increasing the chances of phishing successfully or fraud attempts. These conversations are made possible by analyzing vast amounts of data from social media and other online sources to build detailed profiles of targets.
Financial institutions, technological firms and research entities are aware of these and other possibilities of fraudsters. Many defensive mechanisms and counter-attacks will continue to be released.
2. Advanced Behavioral Analytics
Future AI systems will integrate a combination of hybrid AI models and collaborative networks to anticipate and combat the risks of the future. These systems aim to identify unusual patterns in real-time and predict potential fraudulent actions before they occur. Hybrid AI models will combine supervised and unsupervised learning techniques to enhance our ability to detect known and unknown fraud patterns. As you may know, supervised learning relies on labelled (known) data to train models, enabling them to recognize specific types of fraud that humans are already familiar with. Alternately, unsupervised learning identifies hidden patterns in unlabeled data, uncovering new types of fraud.
In addition, organizations and industries will leverage collaborative AI networks to share threat intelligence to improve overall fraud detection capabilities. The data from these diverse sources will be used to develop increasingly sophisticated self-learning AI models that evolve in real-time with the addition of more data. This collaborative effort will ensure that the AI systems of the future can adapt to threat tactics, and then detect and respond in real-time.
3. Blockchain Technology
Combining AI with blockchain technology will revolutionize fraud prevention by significantly improving security, transparency, and efficiency, and fostering innovation. While there are challenges to overcome, the potential benefits are significant. Let us examine some of the intersecting nodes of AI and Blockchain technologies.
Security
Security of data storage and migration across different banks is crucial. By leveraging the strength of the technology, we can ensure that data is secure and has reliable outputs. This is possible through blockchain’s cryptography, consensus and decentralization principles. Blockchain also facilitates the secure aggregation of AI models trained locally, preventing tampering during the aggregation process.
Privacy
Federated learning (or collaborative learning) is a sub-field of machine learning for training AI models while keeping the data private. This is done by data decentralization. It is important in the processing of sensitive financial data. The combined use of blockchain and federated learning will cause great improvements in privacy.
Collaboration and Decentralization
Blockchain provides a transparent and immutable record of AI decision-making processes, making auditing and verifying AI decisions easier. This is also useful in collaborations. Developers can collaborate securely on a blockchain, combining their strengths to solve complex problems, and secure data exchanges and AI models.
4. Enhanced Biometric Authentication
In banking and finance, popular biometric authentications include fingerprint, facial, iris and voice recognition. They are more convenient and more secure because they are difficult to replicate. There are advantages to using this method, but it can also be insecure if biometric data transmission is not done securely. In such scenarios of insecure data transfer, bad actors can capture an individual’s biometrics and gain access to sensitive data. To prevent such attacks, banks continue to improve their system regularly.
Behavioural biometric patterns which involve typing rhythm and navigation habits will be able to analyze unique user behaviours, ensuring continuous authentication of ownership. Aside from recognitions via AI neural networks, AI algorithms can detect and prevent spoofing attempts, such as fake fingerprints or facial masks, enhancing security. Accessibility for people with special needs will also be improved with the inception of more intuitive and user-friendly biometric systems.
As banks make digital services more secure and accessible, there are steady improvements in biometric authentication and its infrastructure. With the progress in security technology and AI models, this method will revamp the future of fraud prevention.
5. Predictive Analytics and Machine Learning
Fraud detection and prevention are advanced through predictive analytics and machine learning. Predictive analytics simulates fraud scenarios to test the effectiveness of models and continue to learn. In the fight against fraud, high accuracy and efficiency are important. Better monitoring, adaptive learning and risk management are possible with improvements in predictive models. Predictive models will remain crucial in ensuring robust fraud detection and prevention.
6. AI-Powered Cybersecurity Solutions
Various hacker groups have been reported to use AI tools to invent cyber attack tactics. They harness the ability of Large Language Models (LLMs) to
a. Understand, translate and mimic natural languages e.g. engage in discussions to deceive individuals to reveal sensitive information.
b. Automate tasks e.g. generate repetitive spam emails or coding malware
c. Generate code e.g. create malicious scripts
d. Adapt e.g. customized attacks on different domains
e. Gather information e.g. summarize information from large entity to identify vulnerabilities
Researchers progressively work on identifying possible AI uses by cyber attackers to expose them. An example is a recent study by Benjamin Zimmerman and David Zollikofer titled “Synthetic Cancer — Augmenting Worms with LLMs”. This ‘worm’ they created utilized GPT-4 to self-replicate in various files and scan Outlook for emails which are then used in creating customized emails with copies of the malware.
…
Thank you for reading to this point! Artificial Intelligence is an interesting topic and it is crucial to understand the implications on our day-to-day lives and business. In this article, we have shared reasons why AI is important in the banking and finance sector. Real-time transactions and Predictive analytics are a norm now; we can only expect more advancements.
The topic of “AI in Finance” is a series and this is just the first one. Financial fraud and money laundering were addressed in this first part; we hope you have found it enlightening. In PYMNTS Intelligence’s report on anti-fraud efforts of financial institutions shared in an image above, you would notice that the plan with the highest vote of 62% was ‘Improved communication with customers’. This will be our subject topic in the next part of this series, Enhancing Customer Experience with AI.
