How United Overseas Bank (UOB) uses AI

In the AI Verify Foundation's Global AI Assurance Pilot (February to May 2025), PwC tested UOB's internal retrieval augmented generation chatbot, which runs in production for selected staff on Meta Llama 3.1 and answers operational and domain questions from public company documents. The risk assessment focused on model risks. PwC combined rule based scoring for binary and multiple choice questions, embedding similarity for consistency across repeated runs, and LLM based checks of reasoning answers: an LLM split each answer into clauses, an LLM as a judge compared each clause with retrieved passages of the source document to flag contradictions (a clause with no supporting passage counted as a hallucination), and a judge listed the parts of each question left unanswered. Because the production infrastructure was shared with other use cases, outputs were generated manually in a sandbox; because of confidentiality, PwC used its own prompts and ground truths for ten companies, which UOB reviewed. The case study therefore treats the results as a proxy for the production tool and publishes none of them.

No outcome disclosed.

UOB co developed a machine learning anti money laundering solution with Tookitaki that sorts transaction monitoring alerts into three priority tiers and identifies connected parties, so investigators focus on the cases most likely to be suspicious. UOB said in December 2020 that it was the first Singapore bank to apply AI to transaction monitoring and name screening at the same time, working through more than 5,700 alerts a month, and that the model complements its rules rather than replacing them.

• Accuracy: 96%, true positive prediction rate of the high priority tier

  "Since its implementation, UOB’s new AI solution has proven an overall true positive prediction rate of 96 per cent in the ‘high priority’ category"

  Claimed by: organization
• Interactions handled: at least 5700, transaction alerts per month

  "UOB’s AI solution sieves through an average of more than 5,700 transaction alerts each month to flag cases that are more likely to be suspicious with an overall true positive prediction rate of 96 per cent"

  Claimed by: organization

In a six month pilot reported in August 2018, UOB tested Tookitaki's Anti-Money Laundering Suite, with machine learning features co created by the bank, on top of its rule based name screening (against internal and external watch lists) and transaction monitoring, to separate genuine risk from false positives with explainable outputs. UOB reported large false positive reductions on name screening alerts and said it would progressively roll the solution out to customer risk assessment and sanctions screening, which it treats as processes separate from name screening.

• False positive reduction: 60%, six month pilot, name screening alerts on individual names

  "For name screening alerts, there was a 60 per cent and 50 per cent reduction in false positives"

  Claimed by: organization
• False positive reduction: 50%, six month pilot, name screening alerts on corporate names

  "For name screening alerts, there was a 60 per cent and 50 per cent reduction in false positives"

  Claimed by: organization