The Reserve Bank of Zimbabwe’s Financial Intelligence Unit (FIU) is exploring the use of artificial intelligence (AI) and machine learning technologies to strengthen its fight against money laundering, fraud and other financial crimes, as authorities seek to stem illicit financial flows estimated at more than US$6 billion over the past five years.
The move comes as the country’s financial watchdog confronts increasingly sophisticated criminal networks that are exploiting technology, complex financial transactions and cross-border systems to conceal illicit funds.
According to the FIU’s latest National Money Laundering Risk Assessment report, Zimbabwe generated an estimated US$6,15 billion in illicit proceeds between 2019 and 2024, averaging roughly US$1,23 billion annually.
The report identified smuggling, illegal gold and precious stones trading, corruption, fraud, tax evasion and drug trafficking as the main sources of illicit funds.
The losses are equivalent to about 3,4% of gross domestic product, underscoring the significant economic cost of financial crime.
Against this backdrop, FIU director-general Oliver Chiperesa says the institution is modernising its surveillance and analytical capabilities to keep pace with emerging threats.
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“The FIU currently uses various analytical tools, including machine learning-based tools, in line with what most FIUs in the world use,” Chiperesa told businessdigest in an exclusive interview
“The tools currently in use are appropriate and cost-effective for Zimbabwe FIU’s needs. In line with trends around the world, the FIU has started to explore how to utilise AI to enhance its analytical and supervisory work.”
The initiative forms part of a broader digital transformation programme within the FIU.
Chiperesa said the unit had recently recruited specialised information technology personnel to help shape its future technology strategy, including the adoption of AI capabilities aligned with Zimbabwe’s recently launched National AI Strategy.
The development reflects a growing international trend among financial intelligence agencies and regulators. Across the world, AI is increasingly being deployed to analyse vast volumes of transaction data, identify suspicious patterns and flag potential criminal activity in real time.
Traditional monitoring systems often struggle to detect increasingly sophisticated money laundering schemes involving multiple accounts, jurisdictions and intermediaries. Machine learning tools are capable of analysing millions of transactions simultaneously and identifying anomalies that might otherwise escape detection.
The FIU’s latest assessments suggest that such capabilities could become increasingly important in Zimbabwe.
The watchdog has warned that the country's vulnerability to money laundering has increased in recent years, driven partly by the dominance of cash transactions and the size of the informal economy.
The report notes that a substantial share of economic activity continues to take place
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