Credit Insight

JDG Credit Insight helps institutions to predict a new applicant's ability to pay back their loan, with the AI-powered technology and big data sources. Combining rules with the top-notch machine learning, it creates a credit model, which JDGd in finding patterns of loan repayment.

Old school fraud detection

  • Possibility of blocking genuine users The usage of overwhelming rules tends to cause the high rates of incorrect determination of positive result, which may screen genuine applicants.
  • Latency in updating Rules can become invalid when the fraudulent acts changes or updates, which happens often.
  • Heavy maintenance burden The rules-dominated method has a very high requirement of the expansion of the database as the fraud evolves, which demands manual operations, thus resulting in a high cost of time and workforce.

Improving fraud detection with models and rules

A single static rule system is usually limited by fixed thresholds, but a machine-learning system can understand the change of the ideal value for threshold from the data and adapt. Although machine learning has delivered a considerable upgrade to fraud detection systems, it doesn’t mean you should give up using rules completely. An effective anti-fraud strategy should incorporate the benefits of machine learning technology and still include some rules that make sense.

How does JDG Credit Insight work ?

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    Inputting data

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    Extracting related features

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    Detecting fraud through a trained algorithm in datasets

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    Outputting data

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  • Inputting data

    Gather information from different aspects of the applicant. The larger input datasets may lead to more realistic results.

  • Extracting related features

    Analyse applicant's information(e.g.identity information, fraud history, banking status, location and network) to create a database.

  • Detecting fraud through a trained algorithm in datasets

    Search and compare the applicant's related data in our big data sources with a tailored algorithm.

  • Outputting data

    Produce the fraud risk score.

Using JDG Big Data to improve fraud detection
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Datasets

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ML generates features

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The model produces the fraud risk score

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Result: Block/Review/Allow

The model provides each applicant with a fraud risk score on the scale of 1-100 (the higher the score, the higher probability of committing fraud).
The entire process can be tailored to different businesses scenarios.

Use scenarios

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Automated process

Monitor applicator's credit status in a whole lending cycle, assist in making efficient and scientific business decisions.

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Security

Identify potential fraud attack and replace the high-risk customer with the eligible.

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Underwriting and credit scoring

Evaluate credit risk by Integrating score.

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Joint Modelling

Integrate multi-dimension data to establish the scoring model based on a customised scenario.

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Innovative features

Enable continuous optimisation via JDGd feature-derived algorithms.

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Advice providing

Provide professional advice based on hands-on experience.

What makes JDG Credit Insight different?

Real-time response

Analyse customer activity constantly and response immediately when spots the anomaly.

Scalability

Adopt JDGd machine-learning algorithm to handle large datasets, which has good load balancing.

Efficiency

Perform tasks 24/7 automatically.

High accuracy

Determine the result even the fraud acts with non-intuitive patterns or subtle trends.

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Deployment options

Customize solutions to meet the demands of your workload.

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