My research looks at financial crime and risk in financial industry, with a particular focus on trade-based money laundering, AML transaction monitoring and operational resilience. Most of it grows out of problems I came across in practice. After more than 11 years in trade finance, treasury operations and second-line compliance, I’m interested in frameworks that banks can actually apply, not just ones that work on paper.
Abstract
Commercial Banks in an economy play a significant role. Better performance of the banks is always healthy for the economy of the country as a developing banking system enriches the whole economy. This study was carried out with the purpose of investigating the determinants of the profitability of Licensed Commercial Banks of developed and developing nations during the period 2006 to 2022. Secondary data being used in this study were extracted from published and official sources. Simple regressions and multiple regression analysis were applied to analyse the data and relationships. Return on Assets and Return on Equity during the study period was used as the index to measure profitability. Both bank-specific and macroeconomic variables were used as independent variables to measure their impact on profitability. The research findings show a significant correlation between all selected factors and profitability ratios in the banking industry of Sri Lanka, including capital ratio, loans and advances for customers, impairment charge for loans, fee and commission-based income, net interest income, assets size, GDP growth, inflation, and short-term interest rate. In the UK banking industry, all factors except GDP growth show a significant correlation. The study concludes that bank-specific factors have a more significant combined impact on profitability than macroeconomic factors in both countries.
Keywords: Profitability, Licensed Commercial Banks, Bank Specific Factors, Macroeconomic Factors, Panel Data Analysis, Multiple Regression Analysis, Developed vs Developing Economies
Abstract
The banking sector plays a one of the major roles in Sri Lankan economy. Therefore, this sector can turn as one of the life bloods of our economy. Due to the economic importance of the banking sector the Central Bank of Sri Lanka (CBSL) stringently regulated the banking policies and regulations. The main objective of this research problem is “to identify the affect of Bank’s Internal and Macroeconomic Factors on profitability of Commercial Banks in Sri Lanka.”
The key objective of the study is to analyze which Bank’s Specific and Macroeconomic Factors impacts on profitability of Sri Lankan Domestic Commercial Banks (SLDCB) and to study how profitability change over time. Finally, to make recommendations how profitability can be further improved. A comprehensive literature review has been carried out studies of bank profitability across many countries. Studies have broadly found that Bank Specific Factors and Macro Variables have impacted on commercial bank profitability in these countries. This study selected eight factors which influence the profitability of SLDCB. Capital Adequacy, Liquidity, Loan Portfolio, Non-Interest Income and Asset Quality have been used as Bank’s Specific factors and GDP Growth, Inflation and Interest rate used as Macroeconomic Factors. Regression analysis has been done by using panel data of eleven domestic commercial banks over the period of 2006 to 2018 to assess the impact of these factors on profitability as measured by Return on Assets (ROA) and Return on Equity (ROE).
The results reveal that Capital Adequacy, Liquidity, Asset Quality, Non-Interest Income and Loan Portfolio are the key Bank’s Specific Factors which have impact on profitability of SLDCB and as macroeconomic variable, only Interest rate has statistically significant relationship with Bank’s profitability. Bankers can use the analysis and conclusions of this study to improve the profitability of their institutions and also this study included some suggestions for further research.
Keywords: Bank profitability, Commercial banks, Bank-specific determinants, Macroeconomic factors, Panel data analysis, Return on Assets (ROA), Return on Equity (ROE)
Abstract
Anti-money laundering (AML) transaction monitoring systems in UK financial institutions typically generate false positive rates of 85–95 per cent. Compliance teams spend up to 90 per cent of investigation time on alerts that never result in a Suspicious Activity Report. This paper sets out a risk-based framework for reducing false positives without weakening detection: it combines customer risk segmentation, above-the-line/below-the-line threshold tuning, periodic rule rationalisation, and a hybrid model that pairs automated detection with targeted manual review. Disclosed institutional data was not available, so the framework is applied step by step to an illustrative, composite mid-sized UK retail and commercial bank built entirely from published academic and industry benchmarks, not any real organisation’s figures. Applying published threshold-tuning effect sizes to the composite case’s modelled baseline of 800 alerts a day suggests that tuning and rationalisation alone could cut alert volume by roughly one-sixth and free up approximately 81 analyst-hours a day for higher-value review, while keeping the great majority of genuine escalations. The paper also sets out the governance evidence and second-line resourcing this approach needs, and identifies empirical validation against real institutional data as the necessary next step to establish whether these modelled gains would hold in practice.
Keywords: Anti-Money Laundering, Transaction Monitoring, False Positives, Risk-Based Approach, Financial Crime Compliance
Abstract
Trade-based money laundering (TBML) is widely regarded as one of the hardest forms of money laundering to detect, because it hides inside ordinary import-export documentation rather than a suspicious financial transfer. Regulators have responded largely with red-flag indicator lists, while separate and much larger literatures have applied network analysis, machine learning and customs-fraud analytics to adjacent detection problems. This paper reviews these strands to establish what is known about detecting TBML and where the evidence base is thin. Thirty-five academic papers were identified through seven structured searches of an academic database, supplemented by four regulatory and institutional publications, and synthesised thematically. The review finds that regulatory red-flag taxonomies are detailed and well established but designed for manual, document-level review; that TBML-specific peer-reviewed research remains genuinely scarce, a finding independently confirmed by three prior systematic and bibliometric reviews of the field; and that a large, mature and well-validated customs-fraud-detection literature exists on directly adjacent problems, such as under-invoicing and
misclassification, yet is rarely cross-referenced by TBML-specific research. The general anti-money-laundering network-analysis and machine-learning literature reports strong detection performance but is drawn almost entirely from financial transaction or cryptocurrency data rather than trade documentation. The review closes by setting out this gap, a research agenda for closing it, and the methodological limitations of the search process used here.
Keywords: Trade-based money laundering, Literature review, Red flags, Network analysis, Customs fraud detection
Trade-based money laundering is thought to move more than £10 billion through UK trade each year. It’s hard to catch because it hides inside ordinary commercial transactions, and banks often see only the paperwork, not the goods. Most global trade also runs on open account terms, where banks have even less visibility.
UK regulators have raised concerns about firms’ trade finance controls for over a decade, yet the guidance on how to manage this risk is still spread across many different sources. This paper brings that material together. Based on a review of around 80 academic and regulatory sources, it identifies recurring weaknesses in how UK trade finance guards against TBML, including reactive risk assessments, customer checks carried out in silos, and limited scrutiny of the goods being traded.
The paper then proposes a practical, risk-based framework that compliance teams can use to assess TBML risk across their trade finance activity. It’s written with practitioners, regulators and policymakers in mind, and is designed to be tested and developed through further research.
Full details and a link to the paper will be shared here once it’s published.
