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.
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.
Produce the fraud risk score.
ML generates features
The model produces the fraud risk score
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.
Monitor applicator's credit status in a whole lending cycle, assist in making efficient and scientific business decisions.
Identify potential fraud attack and replace the high-risk customer with the eligible.
Evaluate credit risk by Integrating score.
Integrate multi-dimension data to establish the scoring model based on a customised scenario.
Enable continuous optimisation via advanced feature-derived algorithms.
Provide professional advice based on hands-on experience.
Analyse customer activity constantly and response immediately when spots the anomaly.
Adopt advanced machine-learning algorithm to handle large datasets, which has good load balancing.
Perform tasks 24/7 automatically.
Determine the result even the fraud acts with non-intuitive patterns or subtle trends.
Start the conversation with our team. We're always happy to help.