About

AI in Payments & Fraud Risk Management Summit
  1. What is AI?
    • Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
    • The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
    • The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.
  2. What are the benefits of AI in Payments?
    • AI can help to prevent and detect fraud by flagging up unusual transactions.
    • AI tools can detect and monitor unusual behaviours in staff, such as logging on to banking systems out of hours.
    • Another important use case is payments. AI can be used to improve the speed and efficiency of the payment process, by reducing the extent to which humans need to be involved.
    • Banks’ procedures for on-boarding new corporate clients could be much smoother in future if they take advantage of AI technologies to process the vast swathes of documentation required for Know Your Customer (KYC) purposes.
  3. Merger of AI and AML
    • AI holds the keys to a more efficient and transparent AML stance
    • By providing banks with real-time, in-depth analysis of finance streams, AI can provide actionable insights and intelligent anomaly detection at speed.
    • Quickly spotting indicators of fraudulent activity, whether it be an unusually large money transfer to an unfamiliar account or conformation to a known laundering route.
  4. Need For AI to counter Fraud?
    • ML platform can enhance banking fraud detection by helping the data analytics software recognize potential fraud cases while avoiding acceptable deviations from the norm.
    • AI & ML models for fraud detection can also be used to develop predictive and prescriptive analytics software. Predictive analytics offers a distinct method of fraud detection by analyzing data with a pre-trained algorithm to score a transaction on its fraud riskiness.
    • Machine learning can also be used to automatically derive outcome measurements such as a statistical risk.
  5. Challenges of AI in Payments
    • The prediction power of an algorithm is highly dependent on the quality of the data fed as input. Even in quality sources, biases can be hidden in the data. The time and effort required to gather and prepare an appropriate set of data should not be underestimated.
    • The results of intelligent algorithms are opaque and not verifiable. They deliver statistical truths, meaning that they can be wrong on individual cases. The results could have a hidden bias difficult to identify.
    • The use of intelligent machines represents a challenge in terms of liability: who/what shall be responsible in case something goes wrong? Financial institutions are reluctant to give machines full autonomy because their behavior is not fully foreseeable.