As digital payments move faster, financial institutions are facing a new fraud challenge: there is less time to identify risk and even less time to act on it. Real-time fraud detection is changing how institutions approach this challenge, combining AI, risk signals, and automated decisioning to move beyond simply raising alerts toward acting when it matters most.
When Transaction Speed Becomes Risk Speed
Financial transactions that can be completed in seconds leave financial institutions with less room to respond to fraud. When a suspicious transaction is only detected minutes or even hours after it occurs, the opportunity to prevent a loss may already have passed. As a result, the challenge for modern fraud management is not simply identifying risk signals but turning them into decisions and actions before the risk develops into a loss.
The scale of real-time payments in Indonesia shows why these matters. Bank Indonesia reported that BI-FAST processed 1,358.65 million transactions in Q4 2025, worth Rp3,442.26 trillion, representing 30.44% year-on-year growth. As payment volumes continue to grow, speed, availability, and security must move together. The faster money moves, the shorter the window available for institutions to detect and mitigate risk. (Bank Indonesia, 2025)
The challenge is not limited to transaction speed. The scale of fraud response across the industry also highlights how much is at stake. As of 31 March 2025, the Financial Services Authority (OJK) reported that the Indonesia Anti-Scam Centre (IASC) had received 79,969 reports involving reported losses of Rp1.7 trillion. Of the 82,336 accounts reported, 35,394 had been blocked. These numbers point to a practical reality: fraud prevention cannot rely solely on investigation after an incident has occurred. Financial institutions need the ability to identify risk and respond as early as possible. (OJK, 2025)
From Alert to Action: The Limits of Traditional Approaches
Traditional fraud detection typically works by checking transactions against predefined rules and generating alerts when certain conditions are met. Those alerts are then passed to analysts for review. This model remains an important control, but it becomes harder to scale as transaction volumes increase, and fraudsters continue to develop new ways to bypass established patterns.
One challenge is latency. When an alert arrives only after a transaction has been completed, the opportunity to prevent the loss may already have passed. At the same time, adding more rules to capture different fraud scenarios can create alert overload, leaving teams with more alerts to investigate manually. And because fraud’s behavior continues to change, a rule that works well today may become less effective as fraudsters adapt to their tactics.
This is why modern Fraud Detection Systems need to go beyond simply detecting and alerting. They need to assess risk in real time, apply the relevant policies, and determine what should happen next, whether a transaction should be approved, sent for review, or rejected. When human intervention is required, the system should also provide enough context for analysts to understand the situation and decide efficiently.
Why Real-Time Fraud Detection Is Becoming the Foundation
This need becomes even more relevant as Indonesia’s real-time payment infrastructure continues to evolve. Bank Indonesia’s PADG No. 14/2025 strengthened security requirements for BI-FAST. Among other requirements, Financial Services Provider (FSP) must have fraud management capabilities and apply fraud detection technology at both account and transaction level as a first line of defense. The regulation also covers operational monitoring, early warning capabilities, and the handling of anomalous or fraudulent transactions. (Bank Indonesia, 2025)
In this environment, Real-Time Fraud Detection is more than reducing technical latency. It is about putting risk assessment directly into the transaction flow. When a system can evaluate a transaction while it is taking place, an institution has a greater opportunity to mitigate within transaction flow rather than relying solely on investigation after the transaction has been completed.
Sokratech’s product materials distinguish between delayed, near-real-time, and real-time response. In a real-time model, decisions are made with low latency so that action can be taken immediately. Sokratech’s current website positions its FDS for real-time decision-making with decision latency below 250 milliseconds. (Sokratech, 2026)
But speed alone does not determine whether a fraud detection system can make the right decision. The system also needs to understand what is happening around a transaction. This is where AI becomes increasingly valuable.
So, Where Does AI Fit?
A transaction rarely tells the full story on its own. An amount that looks normal in isolation can become more suspicious when considered alongside other signals, such as the device being used, login behavior, transaction frequency, or a sudden change from the customer’s usual activity.
AI becomes increasingly valuable because it can bring these different signals together. By combining transaction, device, session, behavioral, and historical data, a fraud detection system can build a broader view of the activity and produce a more meaningful risk signal.
This raises an important question: how do fraud detection tools learn what’s normal?
In practice, “normal” is not one universal number or pattern that applies to every customer. A behavioral baseline can be built from characteristics such as transaction frequency and amount, timing, device usage, relationships with accounts or recipients, and changes from historical behavior. When these patterns are established, the system can identify activities that begin to deviate from what would normally be expected for that customer.
Sokratech presents this capability through real-time risk scoring and AI-based fraud detection. Its product materials show login data, transaction data, device data, session data, and behavior data feeding into a real-time decision flow that can result in approval, review, or rejection. (Sokratech, 2026)
AI-Based Fraud Detection: Turning Signals into Decisions
AI becomes particularly useful when fraud patterns are difficult to define through manual rules alone. Sokratech’s product materials, for example, highlight scenarios such as multiple small transfers made within a short period, a sharp increase in transaction value compared with a customer’s usual behavior, a large transaction from an account that has been inactive for some time, or a large payment to a first-time recipient. Each signal on its own may not be enough to confirm fraud, but together they can provide a stronger indication of risk. (Sokratech, 2026)
That does not mean AI has to work on its own. Rule-based detection remains important because rules can encode explicit business policies and risk appetites. A practical fraud architecture can bring rules, models, risk scores, and workflows together, allowing AI to identify patterns while giving fraud teams the control needed to apply policies consistently.
The result is a system that can move beyond asking whether a transaction looks suspicious. It can connect the risk signal to the relevant policy and determine what action should follow.
Also Read: Fraud Detection in Banking vs Real-Time Payment Fraud: Who’s Winning the Race?
No-Code Rule Engines Make Fraud Strategy More Adaptive
Fraud patterns can change quickly, so the strategies used to detect them need to keep pace. If every change requires a lengthy development cycle, even a strong AI-powered detection system can struggle to respond quickly enough.
Sokratech provides a no-code rule engine that allows teams to build, test, and deploy rules without always depending on engineering. Rules can use frequency, velocity, duration, amount, match lists, and other conditions. Examples in the product materials include transactions above 300% of a monthly average, more than 14 inbound BI-FAST transfers within 24 hours, or unusually short intervals between login and payment. (Sokratech, 2026)
For fraud teams, this flexibility makes it easier to respond when new patterns emerge. A team can create a rule around a newly identified scenario, test it against historical data, and then move it into a governed production workflow. Fraud strategies can therefore evolve without requiring a development cycle for every adjustment.
Backtesting: Testing AI and Rules Before They Go Live
Being able to respond quickly does not mean every new rule should go directly into production. A rule that is too aggressive may increase false positives and disrupt legitimate customers, while one that is too permissive may allow fraudulent activity to pass through undetected.
Back testing provides a way to balance these two risks. Sokratech allows teams to test rules against historical data before deploying them. Its product materials also describe a custom machine-learning model service that can help train explainable models or use AI to recommend rules. (Sokratech, 2026)
This also creates a practical human-in-the-loop process. AI can help surface potential patterns or recommend candidate rules, while fraud teams retain the responsibility to review and approve them. The strategy can then be tested before it is promoted to a live workflow, keeping automation within a controlled decision-making process.
From Automated Decisions to Controlled Investigation
Not every risk should end with an automatic rejection. Some scenarios require additional context before a decision can be made, making manual review an important part of fraud operations.
Sokratech provides case management to turn alerts into cases, support review, and facilitate approving or rejecting actions through the user interface and webhook. Dynamic list management also allows organizations to maintain blacklist, watchlist, greylist, and whitelist data in real time. (Sokratech, 2026)
These capabilities also need to operate within a clear governance framework. Sokratech’s materials include maker-checker deployment, version control and rollback, audit trails, and role-based access control. Automation, therefore, does not have to mean removing human oversight. Instead, it can help ensure that human intervention is focused on cases where judgment is most valuable, while changes and decisions remain controlled and auditable. (Sokratech, 2026)
Use Case: The Transaction Looks Normal Until Context Is Added
Consider a customer making a transfer whose individual amount is still within a normal range. If the amount is the only signal being considered, the transaction may not trigger an alert. But the broader activity tells a different story: within the previous few minutes, the same account has initiated multiple transfers, used a new device, completed a login and payment unusually close together, and sent a large amount to a first-time recipient.
In a simple monitoring model, these signals may remain disconnected. When transaction, device, session, and behavioral data are evaluated together, however, they can provide a much clearer picture of the risk surrounding the transaction. A risk score can then help determine the appropriate path, approve when the risk is low, review when further investigation is needed, or reject when defined fraud conditions are met.
This is where the shift from fraud alert to fraud action becomes tangible. The value of a modern fraud detection system is not only its ability to identify anomalies, but also its ability to place those signals in context and connect them to the right policy, workflow, and action.
Toward More Adaptive Fraud Operations
As fraud detection becomes more contextual, the role of AI is also evolving. Its purpose is not simply to replace analysts, but to help analysts understand risk patterns, generate risk signals, and recommend rules, while risk scoring, workflows, and case management help guide the decision-making process.
This evolution also connects with the broader conversation around Agentic AI. The concept does not mean that every FDS has become an autonomous agent, but rather that AI can play an increasingly significant role in connecting data, models, rules, policies, and workflows to support more contextual decision-making, while maintaining human oversight.
Sokratech illustrates how these capabilities can come together within a single platform, spanning real-time fraud detection, AI-based risk intelligence, no-code rules, back testing, automated decisioning, case management, dynamic lists, and governance. For banks and fintechs, the goal is not simply to generate more alerts. It is to build fraud operations that can make risk decisions faster, more consistently, and in a way that remains measurable and auditable. (Sokratech, 2026)
From Fraud Alert to Fraud Action
In a real-time payment environment, the gap between detecting fraud and preventing it is becoming smaller. An alert that arrives too late may no longer leave enough room to act. Financial institutions therefore need Real-Time Fraud Detection that can turn risk data and signals into decisions while the transaction is still in motion.
AI can strengthen this process by helping identify behavioral patterns, generate risk signals, recommend rules, and reduce the amount of manual analysis required. When combined with adaptive rule engines, back testing, automated workflows, case management, and governance, these capabilities allow fraud teams to move beyond the question, “Is this fraud?”, toward a more operationally important one: “What is the right action now?”
That is the essence of moving from Fraud Alert to Fraud Action, detecting risk earlier, understanding the context behind it, and taking measures before that risk becomes a loss.
Ready to Move from Fraud Alert to Fraud Action?
Financial institutions need more than a system that generates alerts. With Real-Time Fraud Detection, AI-based risk intelligence, no-code rule building, backtesting, automated decisioning, and case management, fraud teams can build a prevention process that is both more responsive and more controlled.
Explore how Sokratech can help your institution detect, assess, and respond to fraud risk in real time. Contact the Q2 Technologies team, part of CTI Group, to request a demo and discuss the use cases that best fit your business needs.
Author: Jessica Sharon Putranto – Marketing Officer Q2 Technologies
Editor: Wilsa Azmalia Putri – Content Writer CTI Group
