Susan, a tech-averse 70-year-old, recently downloaded a social media app at her granddaughter’s insistence. All is well until she receives a text saying she has won a prize and can claim it by paying a processing fee. Bearing her bank’s name and logo, the message appears every bit genuine. Clueless that it could be the handiwork of fraudsters, the unsuspecting senior citizen bites the phishing bait.
Every day, many like Susan fall victim to financial fraud, a pervasive global threat draining institutions of precious resources and eroding user trust. With manual processes proving inadequate, financial organisations are turning to artificial intelligence (AI) for financial fraud detection. These intelligent systems can analyse transaction patterns and flag anomalies in real time, preventing significant harm.
As per an Interpol estimate, a whopping $442 billion was lost to financial fraud in 2025. Digital payments have spurred the rise in bogus transactions, and scammers are increasingly cashing in on advanced technologies. While monetary loss is the most obvious consequence of financial crime, organisations suffer in many ways—reputational damage, regulatory fines, revenue decline, customer churn, etc.
Different faces of fraud
Surely, a robust defence strategy, including deployment of AI-powered tools, can help businesses and financial organisations safeguard their assets and credibility. But first, they need to possess thorough knowledge of financial fraud. Some common types are:
- Identity theft: This involves illegally obtaining personal information like bank account or credit card details to withdraw funds. Phishing, executed through fake emails or text messages, is a form of identity theft. Phishers often impersonate trusted banks or other entities to gain victims’ trust. A data breach by a hacker would also fall under this category.
- Payment fraud: Involving unlawful money transfer, payment fraud can be executed in ways like phishing or skimming where fraudsters install external devices in ATMs to record card information. Business email compromise is another type of payment fraud. Here, attackers impersonate executives to trick employees into transferring money.
- Investment fraud: Fraudsters usually impersonate financial advisors or claim to be associated with trusted firms and convince individuals to invest in opportunities promising very high returns. Think Ponzi schemes or fake bonds.
- Money laundering: This entails criminals disguising the origins of illegally obtained funds and making them appear legitimate. They use layering techniques and shell companies to do so.
- Cyber-attacks: These are malicious attempts to steal sensitive information by gaining unlawful access to computers and networks. Malware and ransomware are common examples.
With the financial fraud landscape evolving rapidly, criminals have reached unprecedented levels of sophistication. Cases of scammers using deepfake technology to impersonate CEOs and other executives in real time and stealing millions have rattled the business world. According to the Deloitte Center for Financial Services, synthetic identity fraud—blending of real and fake data to fabricate a person or entity—is expected to cause losses of almost USD 23 billion by 2030.
Meanwhile, traditional detection techniques, which rely on rule-based approaches and manual auditing, are failing to keep pace. In such a grim scenario, AI-powered workflow automation represents light at the end of the tunnel. Here is how it helps.
- Automated anomaly detection: AI-based algorithms analyse vast datasets in real time, detecting anomalies and fraudulent activities with speed and precision. They recognise patterns like unusual transaction amounts or location-based purchase inconsistencies.
- Risk scoring: Machine learning (ML) models assign risk scores to transactions/user accounts based on factors such as transaction amount, location, and previous behaviour. For example, a user who usually shops during the day making purchases at 4 AM attracts higher scores. Risk scoring enables organisations to focus on vulnerabilities that pose the greatest potential impact.
- Document verification: AI-powered systems analyse documents at key touchpoints like opening of account or application of loans. These tools accelerate legitimate transactions while identifying subtle inconsistencies that human reviewers may miss.
- Adaptive learning: AI models continuously learn from data and evolving fraud patterns, updating their detection strategies. This ensures organisations stay one step ahead of potential threats.
Major global financial institutions like JPMorgan Chase and HSBC are using AI to reduce bogus transactions.
While AI offers powerful anti-fraud solutions, seamless implementation requires organisations to address certain challenges. First, they should ensure AI systems have access to clean, structured data. Second, data should be obtained/processed ethically, with no violation of privacy laws. Third, they must continuously refine AI models to reduce the risk of false positives, which are genuine transactions flagged as illegal, and false negatives, which are fraudulent activities that go undetected.
There is no disputing that AI is a strong ally. But human judgement remains crucial for investigating flagged transactions and making final decisions. Instead of adopting a man-versus-machine approach, institutions should combine AI’s intelligence with human expertise to get the best of both. Those that build such collaborative partnerships will emerge victorious in the war against fraud.
How Infosys BPM can help
Infosys BPM’s AI-led fraud detection and fraud management solutions empower businesses to fight financial crime with precision and compliance. By using AI, automation and predictive analytics, we drive growth, boost productivity and strengthen risk management. Partner with us to build a resilient, future-ready fraud prevention framework.
