Banking & Insurance

Use Cases

Fraud prediction

Identify fraud patterns, determine the level of risk and predicting future fraudulent events

Creditworthiness analysis

Determine creditworthiness by using historic data to analyze and predict default probability.

Market data cleaning

Detect anomalies (e.g., missing values) and assign abnormal values an averaged or fitted value

Settlement prediction

Predict when a settlement will be effectuated, based on current and historical settlements

Payment matching

Automate matching of incoming and outgoing payments and give the processor choices for “best fit” matches

Client reaction based on scenarios

Analyze client behavior in the different market scenarios and generate simulated market scenario predictions

Asset Store

Ready-to-use solutions for finance & banking

With our Asset Store, there is no need to develop everything from scratch yourself. Benefit from ready-to-use solutions, offering everything you need to kick-start and accelerate your AI projects.

PII Scanner
Governance

The Personal Identifiable Information (PII) scanner gives quick insights about PII within a directory or database

Fraud prediction
Model accelerator

Identify fraud patterns, determine the level of risk, and predict future fraudulent transactions

Bias manager
Governance

Ensure data sources do not lead to unknown or unintended biases

Credit score accelerator
Model accelerator

Determines creditworthiness by using historic data to analyze and predict default probability

Client Case

Fraud detection and prevention

Knowing the likelihood of fraud in a transaction and the drivers behind it allows a company to lower its fraud rate by preventing transactions or conduct more efficient screening. 2021.AI has helped an insurance company with 30,000 cases where fraud was detected in 1,100 cases amounting to a value of 8,880,000 DKK.

1.100

Detected Cases

8.8 million DKK

Fraud Detected

8.000

Average Value Per Case

5%

Estimated Fraud Rate

Calculate Your Own Numbers

Regulatory attention on emerging technologies and their applications is increasing

Organizations working with emerging technologies, such as AI, can expect more external requirements for transparency on how they use them, which will require added governance, risk & compliance capabilities.

Traditional compliance system

When it comes to monitoring and documenting the use of emerging technologies, traditional compliance systems have blind spots:

  • Lack transparent information about how models operate
  • Model results, including whether they are accurate and fair, are not tracked or measured
  • Producing a compliant Data Protection Impact Assessment (DPIA) is not possible
  • Compliance protection by design principles or guidelines are not available
Grace’s Tech GRC system

With Governance, Risk & Compliance measures at its core, Grace makes it easy to monitor and document how emerging technologies are applied:

  • Full transparency on how models operate
  • Solution and risk controls in place, allowing a complete overview of any operational measures of a model
  • Process and solution to ensure that all relevant stakeholders fill in DPIA and other mandatory assessments
  • Solution enhancing compliance with data protection at its core

Solution

Enterprise AI Platform Asset Store Machine Learning Operations Governance, Risk & Compliance

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Finance & Banking Software & Tech Manufacturing Transportation Public Sector Life Science Healthcare

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