Fraud Detection in Banking Payments Reviews and Ratings

What is Fraud Detection in Banking Payments?

Gartner defines fraud detection in banking payments as platforms that use machine learning (ML) models and business rule engines (BREs) to detect and prevent criminal activities related to money movements that aim to defraud the banks and its customers. Banks use these platforms to determine the risk associated with events, such as payments (including real-time payments), and typically include modules for transaction monitoring, a decision engine and case investigations. A high-risk score may initiate a further review to determine if it is a true positive (fraud) or a false positive (not fraud). Modern platforms that incorporate ML models and BREs are capable of monitoring many account actions and use data from multiple sources. They are generally not used for identity verification, internal fraud, physical controls at branches and ATMs or accounts payable functions.

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Founded in 1976, CGI provides strategic IT and business consulting services to companies and government organizations around the world. The company's 91,500 employees work across 21 industry sectors in 400 locations. CGI's mission of providing industry and technology expertise to help meet the needs of customers and citizens has resulted in 40+ years of continuous growth.

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FICO, an analytics software company based in Bozeman, Montana, USA, operates in over 80 countries. The company's focus is to assist businesses in making better decisions that contribute to growth, profitability, and customer satisfaction, using Big Data and mathematical algorithms to predict consumer behavior. FICO offers software and tools that are widely used in various industries for risk management, fraud detection, customer relationship enhancement, operational optimization, and compliance with stringent governmental regulations. Its flagship product, the FICO Score, is a standard measure of consumer credit risk in America. Embracing open-source standards and cloud computing, FICO's solutions aim for flexibility, swift deployment, and cost reduction. Founded in 1956, FICO is an innovator of analytical solutions like credit scoring and other pivotal decision-management technologies such as predictive analytics, business rules management, and optimization.

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IBM is a well-established entity focused on technology and development. The primary mission revolves around fostering technological growth and enhancing infrastructure, achieved through focused developments and consulting services. By encouraging inventiveness and innovation, it is geared towards facilitating the transition of theoretical ideas into practical realities, thus improving global functionalities. IBM brings about transformation by creating advanced solutions that reshape and redefine the world.

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INETCO Systems Limited works with financial institutions and payment service providers worldwide to deliver payments security and reliability. Combining comprehensive transaction data, real-time monitoring, and individualized AI behavioral models, INETCO helps you identify emerging threats and stop fraud and cyber-attacks with accuracy and speed. Outsmart fraudsters, stay compliant and keep customers safe —all without compromise.

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SymphonyAI specializes in AI-based IT Service Management. It combines Service, Asset, and Operations Management to facilitate enterprise productivity. By utilizing machine reasoning and codeless workflow automation, SymphonyAI strives to generate substantial savings in IT Help Desk Operations and improve the Total Cost of Ownership.

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Oracle is a cloud technology company that offers computing infrastructure and software solutions globally. This organization has developed an autonomous database, the first of its kind, to help manage and secure data. Oracle Cloud Infrastructure presents functionalities to facilitate the transition of workloads from on-site systems to the cloud, and vice versa, as well as between different clouds. Oracle's cloud software applications provide modern tools designed to support sustainable growth and resilience in businesses. Tools developed by Oracle are used by a wide range of users including nonprofit organizations and businesses of various sizes, to aid in operations like supply chain streamlining, human resource management, financial planning and connecting data and global users. Apart from business solutions, Oracle's technology also aids in tasks ranging from government defense to scientific and medical research.

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Quantexa is a global entity that aims at transforming decision-making in organizations through its Decision Intelligence Platform. This platform facilitates comprehension of data by connecting disjointed systems and illustrating complicated relationships, thus presenting a comprehensive view of data. This has made it a trusted and reusable tool across different departments in an organization. By using Quantexa's platform, organizations can foster a culture of confident decision-making across strategic, operational, and tactical levels, which facilitates risk management and the discovery of opportunities. To cater to different regional needs, Quantexa is spread globally with offices in places like London, Spain, Amsterdam, Brussels, New York, Toronto, Singapore, UAE, Melbourne, and Sydney.

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SAS is a global leader in AI and analytics software, including industry-specific solutions. SAS helps organizations transform data into trusted decisions faster by providing knowledge in the moments that matter. SAS gives you THE POWER TO KNOW®.

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LexisNexis Risk Solutions is a multi-industry company comprising seven brands. It utilizes innovative technologies, information-based analytics, decision tools, and data management services to aid its customers in problem-solving, informed decision making, compliance, risk reduction, and operation improvement. The brands comprising LexisNexis Risk Solutions cater to different sectors which include aviation, agriculture, chemical and energy, financial services, collections and payments, commercial property, corporations and non-profits, government and law enforcement agencies, healthcare, human resources, insurance, and tax. These brands process data and develop technology solutions to generate insights that can help businesses and government entities. The company is headquartered in Atlanta, Georgia, with offices situated worldwide, and is a part of RELX, which delivers information-based analytics and decision tools for professional and business customers on a global scale.

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Features of Fraud Detection in Banking Payments

Mandatory Features:

  • Case investigation (CI): Transactions highlighted by the DE as high risk need to be assessed by human case investigators. Bank staff will use the CI module to make an informed judgment about whether a particular transaction is a true positive (fraud) or a false positive (not fraud). The CI module will give access to additional data sources such as know your customer (KYC) or watchlists to help make the judgment. There is always a capability for posting a report that states the judgment with an audit trail for how that decision was arrived at. To increase productivity and accuracy, case investigation modules may include additional capabilities such as automated workflow, AI assistants, smart allocation, triage for urgency/importance, data source prioritization and prepopulated reports.

  • Orchestration: This applies to both data and processes. There must be a means of importing the right data, from the right systems and sources, at the right time, and then processing them in the right order. This is the “glue” that integrates the preceding three modules. Most vendors will have built their own orchestration capability, or else they will have adapted a standard business process management (BPM) tool for this specific purpose.

  • Decision engine (DE): This module must include at least one (and usually both) of an ML model and a BRE. The DE will process data accessed via the TM module. It will also need a set of API connectors for ingesting additional data from security systems such as device ID, location intel and behavioral biometrics. Some of these APIs will be standard off-the-shelf connectors for common systems and others will be custom-made for a specific deployment. The combination of ML and BRE is used to highlight suspected fraudulent payments and money movements by calculating a risk score for each transaction. The ML may be imported from the bank (if it has a preexisting model) or it may be supplied off-the-shelf by the vendor and then tuned for the specific bank it is deployed at.

  • Transaction monitoring (TM): This module is capable of ingesting data for financial events in the form of (a) incoming and outgoing payments and (b) money movements such as transfers between accounts held at the same bank or between products held by the same customer. It must have links to the payments hub (or payment systems) at the bank and also its core banking system.