Fraud detection
AI helps organisations spot unusual behaviours in their systems by analysing real-time data and learning the difference between normal and suspicious patterns. For example, a bank might automatically flag an account that suddenly engages in multiple small and quick purchases initiated from different countries. At PwC, we helped a financial institution implement machine learning models that analyse transaction data in real time, detect outliers in spending patterns and alert responsible parties to potential fraud, allowing them to quickly identify cases that would require deeper analysis.
Credit risk assessment
Lending money or extending credit always carries some level of risk, and AI can help companies make more informed decisions by analysing past transaction data, credit histories and external market indicators. Banks, for example, might want to predict an applicant’s ability to repay a loan, while businesses can assess the financial reliability of potential suppliers. At PwC, we worked with an insurance firm to develop a model for calculating experience rates for corporate customers, addressing challenges like data scarcity and high levels of aggregation. By blending general industry rates with a customer’s specific claims history, the model provided a more accurate risk assessment.
Why is master data governance important?
Consistent financial master data (e.g. chart of accounts, customer/vendor information) ensures accurate reporting, compliance and transparency. It simplifies consolidation across different business units and facilitates smooth audits.
Source SAP tables
General Ledger Accounts (e.g. SKA1, SKB1)
Cost Center (e.g. CSKS)
Profit Center (e.g. CEPC)
Customer and Vendor (e.g. KNA1, LFA1)
