Risk

AI algorithms can analyse vast amounts of structured, unstructured, and cross-siloed data from the history of risk cases to gain a more complete view of individuals or organisations to identify the downside of issuing credit, early signs of potential malfeasance, or even systemic risks such as supply chain disruptions. Aicadium is helping customers efficiently identify and mitigate risk through fraud detection, credit risk scoring, and detection of other risk-generating occurrences.

Credit Risk Scoring

Combining data sets to develop a more holistic view of individuals or organisations enables real-time intelligent lending decisions such as data driven credit risk assessments to minimize downside risks of issuing loans. Cloud-first enterprises are also harnessing alternative data sets, such as telco data, in decision making for credit risk scoring and loan decisioning models. AI-driven credit risk scoring can help both bank and non-bank players utilise this alternative to assess the creditworthiness of individuals or entities.

Fraud Detection

Anti-fraud AI can predict which customers are most vulnerable to fraud and enable organisations to intervene in order to prevent fraudulent transactions being made. Anti-fraud models reduce losses for a bank’s customers, in turn improving customer confidence and satisfaction. It also reduces resource costs allocated to managing fraudulent cases and transactions.

Risk Assessment

AI provides the ability to evaluate both structured and unstructured data about risky behaviors or activities in an organisation’s operations. AI algorithms can identify patterns of behavior related to past incidents and transpose them as risk predictors.

Case Studies

  • Risk

  • Industry

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