By Musammad M Tamanna

From banking transaction reviews to research on AI-powered anomaly detection, Tonay Pal has examined how better data analysis can strengthen financial controls. Drawing on experience in banking, procurement and compliance, he discusses the worldwide challenge of identifying suspicious activity early, without mistaking every anomaly for fraud.

Q: Why is financial fraud detection a global priority?
Money moves rapidly through banks, businesses and digital platforms, often across borders. Suspicious activity can be hidden among thousands of ordinary transactions, invoices and account records. Manual checks remain important, but the volume and complexity of data create a need for more systematic detection. The real challenge is identifying meaningful irregularities early while allowing legitimate financial activity to continue.

Q: How did your professional background lead you into this field?
At Janata Bank in Bangladesh, I reviewed more than 10,000 customer transactions for accuracy and unusual entries. Later, at AG Agro Industries, I checked supplier invoices against purchase orders and goods receipts before payment. Those roles showed me the value of catching discrepancies before funds move. My graduate training in management information systems helped me connect that experience with data analytics.

Q: What are the most useful warning signs of possible fraud?
Duplicate payment requests, unexplained changes to payment details, inconsistencies between invoices and supporting records, and unusual transaction patterns can all deserve attention. But a red flag is not proof. Some irregularities are honest errors or normal exceptions. A responsible detection process asks what needs to be verified and preserves the evidence behind that decision.

Q: How can artificial intelligence improve fraud screening?
AI and machine learning can help detect combinations of unusual signals in large datasets and prioritize cases for human review. My 2026 research article examines AI-based decision support and anomaly detection in financial risk management. These methods can complement established controls such as reconciliations and payment verification. An algorithm should inform an investigation, not automatically pronounce someone guilty.

Q: What separates a strong detection system from one that merely produces alerts?
A useful system needs reliable data, clear indicators, timely review, an audit trail and understandable reasons for every alert. Detection rates alone are not enough. Organizations must also measure false positives-legitimate activity incorrectly flagged as suspicious-and the time required to resolve each case. If reviewers cannot trust or explain the alerts, the technology may add work rather than reduce risk.

Q: How can organizations reduce false alarms?
They should avoid treating any single unusual feature as a verdict. Combining indicators, checking supporting documents and adjusting thresholds to the actual business process can improve decisions. False alerts waste staff time and can delay valid payments; missed fraud creates another kind of risk. The aim is a practical balance supported by testing, documentation and independent human review.

Q: Could the same fraud-detection model work in every country?
Not without careful local testing. Payment systems, legal requirements, record quality and fraud patterns differ between jurisdictions. What can travel more readily is the discipline behind the model: transparent indicators, privacy safeguards, repeatable tests, documented errors and clear responsibility for decisions. A tool that performs well in one setting should not be assumed to work equally well elsewhere.

Q: What can smaller businesses do without expensive AI software?
Start with dependable records and basic controls: reconcile transactions, compare invoices with orders and receipts, define payment approvals and document exceptions. Spreadsheets and dashboards can make repeated discrepancies easier to notice. Analytics can then be added gradually, with evidence that they actually improve detection or review time. Good controls should come before complicated technology.

Q: What are you planning to develop next?
My first planned project is a documented fraud-indicator library, a synthetic test dataset and practical screening templates in Excel and Power BI, with an optional Python version. It begins in a defined public-funding compliance setting, rather than claiming to cover every form of financial fraud. I plan to measure detection, false alerts and reviewer effort. The toolkit is not yet completed or deployed, and applications in other sectors would require separate validation.

Q: What should shape the future of fraud detection worldwide?
It should be proactive, explainable and fair. AI can help people recognize patterns sooner, but financial decisions must remain grounded in evidence, oversight and accountability. Success is not measured by the number of alerts produced. It is measured by how well organizations investigate real risks, prevent improper payments where possible and protect confidence in financial systems.

Biography
Tonay Pal holds an M.S. in Management Information Systems from Lamar University and an MBA and BBA from the University of Dhaka. His professional experience includes banking transaction review, procurement payment verification and financial compliance analysis. His research addresses AI-based decision support and anomaly detection in financial risk management. Email: paltonay@gmail.com