There are three things in the world that deserve no mercy – hypocrisy, fraud, and tyranny. - Frederick William Robertson, British Theologian.
While Robertson may have been speaking from a 19th-century theological lens, his observations are as true today in the 21st. Fraud as an activity has assumed gigantic proportions in the digital era, with technically-savvy fraudsters employing a panoply of tools and stratagems to defraud people and organisations.
Enter fraud orchestration. This is a centralised, data-driven strategy used to detect, analyse, and prevent digital fraud in real time. It unifies data from multiple sources, including transaction logs, user profiles, and threat intelligence, into a single platform to enable coordinated responses across channels.
The technology integrates data analytics, machine learning, and automation to identify anomalies and patterns in data proactively. Orchestration is an integrated activity: unlike siloed tools, fraud teams typically use a "mission control" view of all the insights that are derived from the data they are parsing (often in real time). This allows them to rapidly deploy defenses to prevent fraud without disrupting legitimate transactions. Think of teams working to prevent online shopping scams, credit card fraud, ransomware, phishing attacks, or identity theft: all of these, and more, come under the domain of fraud teams.
How it works
Fraud orchestration works by centralising data ingestion, analysis, and automated decision-making in a unified platform to detect and respond to threats in real time. Multiple signals—such as transaction details, user behavior, device data, and external threat intelligence—are combined via data integration, scoring, and rules-based orchestration to produce a single risk decision.
The three key elements that factor into fraud orchestration design are:
- Data unification from diverse feeds
- real-time decisioning engines for risk scoring, and
- adaptive workflows that evolve with fraud tactics.
The process begins with data unification from diverse sources like logs, profiles, and analytics feeds into a central hub. Advanced tools, including machine learning, behavioral analytics, and Robotic Process Automation (RPA), then process this data for pattern detection and anomaly identification.
Next, the system triggers rapid, adaptive responses such as blocking transactions or requiring additional verification. This minimises false positives while adapting to new threats.
Orchestration systems aggregate multiple signals holistically: for instance, a login from a new device (signal 1) is matched against an unusual transaction frequency from the same account (signal 2), and mismatched behavior such as a new kind of purchase (signal 3). The three signals are then scored together via a decision engine. With this, the system arrives at a composite risk score that can lead to one unified action: either approve, challenge, or decline, rather than isolated checks.
Benefits of deployment
The benefits of building and deploying fraud orchestration systems are many:
- Fraud orchestration enables enterprise-wide visibility by offering a single, unified view of fraud signals across the entire organisation.
- It also enables faster mitigation of risks: the longer a fraudulent transaction or pattern goes unaddressed, the more damage it causes. Traditional setups often have siloed tools — the payments team uses one system, the identity team uses another, and so on. When fraud spans multiple channels, nobody sees the full picture quickly enough to act.
A fraud orchestration system connects all these signals in one place, so when a suspicious pattern emerges, the system can trigger a response automatically and immediately, by blocking a transaction, stepping up authentication, or flagging an account. This is a speedier risk mitigation strategy over waiting for a human to notice, escalate, and coordinate a response across teams. - The systems enable revenue protection by testing targeted hypotheses: fraud controls, if too aggressive, may block legitimate customers (who may be travelling or making an unusual purchase), which can cost businesses in revenue and reputational damage. With hypothesis-based A/B tests on fraud rules, teams can find the sweet spot that lessens false positives without meaningfully increasing fraud losses.
- In sectors like Banking and Financial Institutions (BFSI), orchestration synchronises tools for holistic defense of digital financial products, reducing silos and boosting efficiency. The sector uses the technology to combat multichannel fraud, with a goal of simultaneously minimising false positives and simultaneously preserving customer experience.
Stemming utility thefts
Fraud orchestration systems can be particularly effective in utility theft monitoring. The systems can be deployed to detect electricity, gas, or water theft by consumers who are bypassing meters or tampering with usage readings. In this context, the system integrates real-time data from smart meters, consumption patterns, and anomaly detection to flag suspicious activities like zero usage or irregular spikes.
Multiple signals — such as tamper events logged in meters, sudden drops in consumption, or mismatches between neighbourhood or historical averages and individual accounts — are combined into a unified risk score on the central system. The system then triggers automated alerts that may help utility providers to plan field inspections. If the inspectors detect tampering or fraud, they could recommend or put into action subscription cancellations, recovery of dues, or legal recourse. The activity thus prevents revenue loss for utility providers.
Some of the detection techniques include:
- Smart meter tamper logs that have timestamps for any physical bypasses. A meter tamper event, such as the opening of the meter door, could trigger an orchestration event like remote disconnect.
- Analytics reports that spot outliers like low readings during in-demand seasons. For example, a low kw/H reading on an electricity meter during peak summer could trigger an inspection.
- Integration with grid data for non-technical loss identification. For example, a below-cohort (neighbourhood) average billing is a pattern mismatch that could trigger a risk score escalation.
How Infosys BPM can help
Infosys BPM offers industry-leading solutions to combat fraud management, particularly in the financial management industry. The solutions comprise the latest technologies, including AI-powered analytics, to facilitate swift financial crime detection and proactive risk mitigation.
