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.
Gathering finance-related information
Gather information from transactions, orders and banking status to predict fraud.
Analysing applicant's online behaviour
Analyse applicant's behaviour such as ordering time and browsing time on the website.
Calculating the real-time rate of fraud
Give the real-time rate of fraud via categorical data. It is particularly beneficial for market expansion.
Discovering the similarity
Compare the similarity of a customer's individual information today with the past.
Extract and record genuine customer behaviour when making payments.
Entity dynamic analysis
Analyse entity-related information to alert fraud attack. For example, analyse the orders' quantity of a specific IP-address.
Creating a derived network
Build a derived network to enhance customer data, and enables its users to manage high-risk transactions, share data, and gain intelligence from fraud analysis.
AI-based data collection
Combining AI-powered technologies of text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing, it offers a strong and reliable data-driven solution to prevent fraud.
Improve profitability while decreasing costs.
Provide a suitable product and service to customers with different business purposes.
Enhance decision-making process and optimise portfolio management and risk decisions.
Prevent fraud attack and monitor fraud trends.
Reduce debt recovery cost and maximise the returns.
Evaluate credit status based on telecom behaviour.
ADVANCE.AI's cross-platform database provides risk prediction from multiple dimensions for various business scenarios.
ADVANCE AI's big data service provides stable data resources and reliable data analysis to assist in risk management.
Provide data-driven technical support to various industries and optimise user experiences based on the business requirements of Southeast Asian users.
Professional expertise is offered to customers from our top experts.
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