BPM Analytics

Fraud detection technologies: Make AI and machine learning work for you

According to a global financial services firm, 53% of the respondents experienced fraud causing direct financial losses. Indirect negative outcomes of the fraud were loss of new business opportunities (24%), lower employee morale (23%), damage to the brand’s reputation (36%), and regulatory actions such as penalties (23%).

Cybercriminals are innovating to find opportunities to exploit gaps in the financial systems. They inflict significant damage to the financial institution and its customers by stealing and selling personal information on the dark web or causing direct financial loss. The problem is so widespread that only 46% of US citizens trust financial institutions to safeguard their data.

This article explains the role of artificial intelligence (AI) and machine learning in vigilance and fraud detection in the financial services industry.

How does AI help in financial fraud detection?

Fintechs in the US lose $51 million annually to fraud. This is where AI fraud detection comes to the rescue. It sifts through large datasets, identifying patterns and possible fraud instantly and accurately. Unlike pre-programmed rules, AI uses cognitive computing with dynamic learning capabilities to identify theft and scams in these ways –

Automated anomaly detection

AI algorithms use patterns to identify fraudulent activity, such as unusual transaction amounts or multiple transactions, and flag it for investigation.

Behavioural analysis

AI fraud detection monitors a customer’s purchasing behaviour over time and flags if there is a sudden large transaction.

Natural language processing (NLP)

AI uses NLP to analyse customer communications such as emails and chats for potential fraud.

Dynamic learning

As criminal modus operandi evolve to find gaps in fintech systems, AI fraud detection algorithms use new data input as a fresh training ground to improve their accuracy over time. Thus, the algorithms learn from the latest trends and tactics of online fraud.

Machine learning models for fraud detection

Four prominent machine learning models help in developing robust fintech fraud detection systems.

Unsupervised learning

They analyse indicators from past fraudulent activities to detect unusual events. However, this analysis flags the anomalies for further investigation and does not confirm fraud. For example, unsupervised learning models ingest and analyse bank statements and flag for an investigation if there is a change in the format or font.

Text analytics extract and categorise names of people and companies, connections, and monetary values. For example, excessive numbers in the routing number or inconsistencies in the account owner’s name and associated address in a fake cheque are flagged.

Supervised learning

Supervised learning models use human feedback and assistance to determine if a behaviour is fraudulent or not. This helps machine learning models operate independently and identify patterns. Supervised learning helps detect credit card fraud, telecommunications fraud, medical insurance fraud, and automobile claims fraud.

For example, supervised learning models detect cellular clone fraud using a hybrid learning approach that combines human expertise, integrated statistics, and data mining. The rule-learning program uses a vast customer transaction database to detect fraud.

Semi-supervised learning

It combines supervised and unsupervised learning models to label available data when it is costly or impractical. Humans label specific portions of data to enhance fraud detection. While the unlabelled data helps in validation, labelled data helps in training the model.

Reinforcement learning

Reinforcement models learn through trial and error to find the most cost-effective solution. The model repeatedly executes different actions and acquires the knowledge for optimal behaviour. By leveraging feedback from rewards and punishments, the algorithm distinguishes between favourable and unfavourable actions to identify those that present maximum rewards with minimum risk.

Advantages of AI fraud detection

By incorporating AI in fraud detection, Fintechs can achieve the following benefits –

Better accuracy

AI algorithms analyse large quantities of data to detect patterns and anomalies that are difficult for humans. This increases the accuracy of fraud detection at scale.

Real-time monitoring

Monitor real-time transactions and handle credit card fraud, fake accounts, account takeover, and credential stuffing as they happen, thus minimising the potential damage.

Low false positives

False positives happen when the system flags a legitimate transaction as a potential fraud for investigation. AI algorithms reduce false positives significantly, allowing reliable fraud detection.

Greater efficiency

Automate repetitive tasks such as data entry, transaction reviews, and identity verification, and utilise your team for tasks that require human intervention and analysis. Use AI fraud detection to streamline operations and improve efficiency with minimal manual intervention.

Cost reduction

Fraud not only inflicts financial damage but also harms the reputation of the company. AI algorithms are cost-effective ways to prevent financial fraud before it causes major financial damage.

For organisations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed organisational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like living organisms will be imperative for business excellence. A comprehensive yet modular suite of services is doing precisely that. Equipping organisations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organisations that are innovating collaboratively for the future.

How can Infosys BPM help?

Make your financial services business robust and resilient to fraud, such as compromised credentials, theft and leakage of personal information, and fake emails and calls with the power of AI.

Read more about fraud solutions in finance industry for the fintech industry.