Near-Real‑Time Credit Card Fraud Detection Solution Framework built on Databricks Lakebase and Genie

Executive Summary

Financial institutions today face mounting pressure to detect and prevent fraudulent card transactions at the very moment they occur, rather than relying on detection methods during post-authorization or settlement stages. However, many banking systems continue to operate on legacy batch-processing architectures that were not designed for real-time decision making. As a result, fraudulent transactions often go undetected, leading to increased financial losses and in some cases, unnecessary disruptions to legitimate customer transactions.

Beyond latency, these legacy systems lack transparency in decision making. They struggle to provide clear explanations of how fraudulent activities occur and fail to offer meaningful insights into the factors driving fraud detection outcomes. This lack of explainability can erode trust, create challenges for regulatory compliance, and make it difficult for institutions to continuously improve their fraud prevention strategies.

This blog introduces a reusable, metadata-driven near-real‑time credit card fraud detection solution framework built on the Databricks Data Intelligence Platform. The framework is designed to identify fraudulent transactions in near real time, enabling institutions to take corrective action before authorization is completed.

The solution framework adopts a streaming-first architecture, processes data on a fully governed Lakehouse, and delivers low‑latency feature access through Databricks Lakebase. DLT serverless compute is used to elastically scale transaction-level processing while maintaining consistent performance under variable load. Fraud scoring combines machine learning models with configurable business rules, enabling institutions to balance predictive accuracy with regulatory and operational constraints.

Beyond detection, the solution framework places strong emphasis on explainability and analyst interaction. With Databricks Genie, fraud teams can query transaction patterns, fraud drivers, and model outcomes in natural language, making it easier to understand why a transaction was flagged and where control gaps exist. This shifts the system from a black‑box scoring engine to an insight-driven decision platform.

The reference architecture illustrates how Databricks supports transaction-time analytics and decisioning within a single governed environment, eliminating the need to separate real‑time operational systems from analytical platforms. Although demonstrated through a credit‑card fraud detection use case, the framework can serve as a blueprint for a wide range of near-real-time risk and decisioning scenarios. Overall, the blog highlights how organizations can move from reactive fraud investigation to proactive, explainable fraud prevention at scale.

Customer Challenge: Fraud Decisions Happen After the Damage Is Done

For most financial institutions, fraud detection still operates after a transaction has already been authorized. While post‑transaction analytics help identify fraud patterns and recover losses, they fall short where it matters most—preventing fraudulent activity at the moment of payment.

Legacy fraud detection systems are typically optimized for batch processing and retrospective analysis, resulting in delayed decision‑making. At the same time, transaction data, customer behavior history, and risk signals are distributed across multiple systems, making it difficult to assemble a real‑time view of risk. As a result, organizations face a tradeoff between fraud prevention and customer experience.

Common challenges include delayed access to features, fragmented OLTP and analytics platforms, rigid rule engines with high false‑positive rates, and limited explainability once a transaction is flagged. Operations teams often struggle to answer a simple but critical question: why was this transaction considered fraudulent?

As payment volumes increase and fraud tactics evolve in real time, financial institutions need a new approach—one that delivers decision‑grade intelligence at transaction time, not after the fact.

Introduction: From Post‑Transaction Analytics to Transaction‑Time Intelligence

Modern fraud prevention requires more than faster models. It requires bringing real‑time decisioning, AI, and analytics onto the same platform. Rather than moving data across disconnected streaming systems, feature stores, decision engines, and analytics warehouses, organizations need a unified architecture that can handle all of it—securely and at scale.

The Databricks Data Intelligence Platform enables this convergence. By unifying streaming ingestion, large‑scale analytics, machine learning, and governance within a single Lakehouse architecture, Databricks allows fraud decisions to be made while the transaction is in flight.

With Databricks Lakebase, real‑time fraud use cases can now access low‑latency features and state without duplicating data into external operational stores. Combined with serverless Delta Live Tables (DLT) and built‑in ML lifecycle management, Databricks makes near-real‑time fraud detection operationally feasible and architecturally streamlined.

This blog introduces a near-real‑time credit card fraud detection solution framework that demonstrates how financial institutions can modernize fraud decisioning using Databricks—delivering faster decisions, better accuracy, and explainable outcomes.

Solution Overview: A Near-Real‑Time Fraud Detection Solution Framework on Databricks

The solution framework is designed as a reusable architecture pattern, rather than a one‑off implementation. Its objective is to score transactions in near real time, enable intervention before authorization, and continuously improve fraud detection outcomes using a single governed data foundation.

At a high level, the solution delivers:

Streaming‑first ingestion of transaction events
Near-real‑time feature engineering at transaction scale
Low‑latency feature access with Databricks Lakebase
Machine learning–driven fraud scoring
Natural language explainability with Databricks Genie

Streaming Ingestion and Processing

Credit card transactions are ingested as a continuous event stream and processed using Databricks Structured Streaming. This enables low-latency processing while maintaining strong reliability and exactly‑once semantics.

Feature Engineering with Delta Live Tables (DLT)

Delta Live Tables provides a serverless, declarative framework to transform raw transaction streams into fraud‑ready features. Streaming and historical signals—such as spend velocity, geolocation patterns, merchant behavior, and customer risk history, are continuously updated and validated.

By automatically managing orchestration, scaling, and data quality checks, DLT enablesfraud teams to focus on feature logic rather than pipeline operations.

Online Feature Access with Lakebase

Transaction‑time fraud detection requires millisecond‑level access to recent customer and card activity. Databricks Lakebase enables this by serving as a low‑latency operational layer fully integrated with the Lakehouse.

Features and transactional state stored in Lakebase remain governed, consistent, and immediately available for both online scoring and offline analysis, eliminating the need for separate feature stores or operational databases.

Real‑Time Scoring and Decisioning

Machine learning models trained on historical fraud patterns generate a risk score for each transaction. This score is combined with configurable business rules to enforce regulatory controls and business thresholds.

The result is a decision that can be acted upon immediately—approve, challenge, or decline—before the transaction is authorized.

Explainability with Databricks Genie

Trust and transparency are essential in fraud detection. Databricks Genie enables fraud analysts and business users to query transactions, risk scores, and contributing features using natural language.

Teams can quickly understand why a transaction was flagged, how customer behavior deviated from normal patterns, and which signals drove the final decision—all without custom dashboards or complex SQL.

Conclusion: Proactive Fraud Prevention with Databricks

Fraud prevention is fundamentally a timing problem. The value of analytics drops sharply once a transaction is complete. To stay ahead of increasingly sophisticated fraud schemes, financial institutions must shift from reactive investigation to proactive, transaction‑time decisioning.

This near-real-time fraud detection solution framework shows how Databricks enables that shift. By unifying streaming data, AI, low‑latency feature access, and explainability on one governed platform, organizations can stop fraud before it happens—while improving customer experience and operational efficiency.

Beyond credit card fraud, this solution framework serves as a foundation for other transaction‑time use cases such as payment risk management, credit assessment, and anomaly detection. With Databricks, real‑time intelligence becomes a built‑in capability of the data platform—not an afterthought.

A Solution Framework for Real-Time Claims Processing, built on Databricks Lakebase

Why the Industry Needs Real-Time Claims Processing

Insurance claims processing is a critical function for insurers, covering the full lifecycle from claim submission and validation to adjudication and settlement. It involves multiple stakeholders who rely on timely, accurate information to act with confidence.

In today’s digital-first environment, policy holders expect instant claim updates and faster settlements — without compromising operational efficiency or compliance. However, many traditional claim systems remain fragmented and batch-driven, resulting in delayed approvals, inconsistent data views, and reactive decision-making. These limitations reduce visibility, slow down operations, and impact customer experience in an industry where responsiveness and transparency are key differentiators.

The Problem: Fragmented Systems and Delayed Decisions

Most insurance enterprises still operate on fragmented architectures where claims platforms, underwriting tools, and analytics environments function in isolation. This creates several critical challenges:

Batch-driven processing: Claims are processed in periodic cycles rather than in real time, delaying approvals, settlements, and overall response times.
Data silos and inconsistency: Disconnected systems maintain separate copies of data, resulting in inconsistent views across channels and reduced trust in information.
Reactive decision-making: Fraud detection, risk evaluation, and actuarial calculations often occur after transactions are completed, increasing financial exposure.
Complex data movement: ETL pipelines are required to move data between operational systems and analytics platforms, introducing latency and operational overhead.

Together, these limitations make it difficult for insurers to deliver a responsive customer experience while also increasing cost, complexity, and risk.

The Solution Framework: A Lakebase-Centric Approach

To address these challenges, Impetus designed a solution framework for real-time claims processing, built on Databricks Lakebase, that unifies transactional operations and analytics on a single, real-time platform.

In this solution framework approach, all core business transactions—such as customer updates, claim submissions, and payment processing—are captured directly in Lakebase, which serves as the operational system of record. Lakebase provides low-latency, ACID-compliant processing with high concurrency, ensuring that data remains consistent and immediately available.

Unlike traditional architectures that separate operational and analytical systems, this solution framework keeps data within a single platform and changes are synced into the Databricks Lakehouse. This eliminates the need for complex ETL pipelines and ensures that analytics and AI operate on fresh, consistent data.

Architecture Overview: Unified Operations and Analytics

The solution framework architecture is designed to seamlessly connect business applications, transactional processing, and analytics:

Frontend applications: Customers, agents, and partners interact through web and mobile applications for activities such as claim submissions.
Databricks Apps (API layer): Acts as the backend layer, handling API orchestration, validation, and business logic.
Lakebase (operational layer): Stores all transactional data—including customers, claims, and payments—and ensures real-time updates across the system.
Business engines: Claims, underwriting, and payment engines operate directly on Lakebase to execute business rules and decisions.
Lakehouse (analytical layer): Data from Lakebase is synced into the Lakehouse and organized into Bronze, Silver, and Gold layers for analytics and reporting.
Analytics and AI layer: Enables dashboards, fraud detection, risk scoring, and actuarial insights using tools such as Databricks SQL and MLflow.

The outcome is a single data foundation that supports both operational and analytical workloads, enabling real-time processing and decision-making.

Key Capabilities: What This Enables

By combining Lakebase with the Lakehouse, it delivers several powerful capabilities:

1. Real-time claims processing: Claims can be submitted, validated, and processed instantly, shortening settlement cycles and lifting customer satisfaction.

2. Unified data platform: Operational and analytical data coexist in the same platform, eliminating data duplication and ensuring a single source of truth.

3. Instant insights on live data: Fraud detection, risk scoring, and actuarial calculations can run on live transactional data rather than on delayed datasets.

4. Elimination of ETL overhead: Removing complex data pipelines reduces latency, infrastructure cost, and operational complexity in one move.

5. High-concurrency processing: The platform supports simultaneous reads and writes, enabling real-time interactions across customer portals, underwriting systems, and analytics tools.

Business Impact: Transforming Claims Processing

The solution framework shifts claims processing from a reactive, batch-driven model to a proactive, real-time model, delivering measurable business benefits:

Faster claims settlement: Reduced turnaround time improves customer experience and satisfaction.
Improved risk management: Real-time insights help detect fraud and manage exposure proactively.
Operational efficiency: A simplified architecture reduces maintenance and integration costs.
Better decision-making: Access to live, consistent data enables faster and more informed business decisions.
Regulatory compliance: Unified governance improves traceability, auditability, and control.

Overall, insurers can operate more efficiently while delivering a more responsive and transparent experience to customers.

Conclusion: From Systems of Record to Systems of Action

Traditional systems were designed primarily to record the transactions. Modern insurers, however, need platforms that can act on those transactions instantly. By leveraging Lakebase as the operational backbone and the Lakehouse for analytics and AI, this solution framework bridges that gap, enabling real-time decisioning on a unified data platform.

The result is not just an architectural transformation but a fundamental shift in how claims processing are executed—from delayed processing to real-time, insight-driven action.

The Context Gap: Why Your Agentic AI Investment Isn’t Paying Off (Yet)

Enterprises are pouring significant investments into agentic AI, yet many aren’t seeing the returns they expected. The underlying issue isn’t the technology — it’s the ‘context gap’: AI models lack the enterprise-specific knowledge (customer data, industry processes, internal workflows etc.) needed to deliver real business value.

In a Forbes article, Nachiket Deshpande, the Chief Executive Officer of Impetus Technologies, outlines why agentic AI can deliver real ROI only when models are grounded in business context, not just capability.

Key takeaways

Enterprise-specific context is critical for agentic AI success: AI must be grounded in company data, processes, and customer context to perform reliably.
Getting AI into production is not the finish line: Many organizations achieve deployment but fail to translate this into measurable business outcomes.
Missing context = higher cost: Without the right context foundation, agents iterate more and consume more tokens, impacting costs and scalability.
Context cannot be an afterthought: It must be engineered into the ecosystem from the beginning – right from the data catalog and semantic layer to governance controls.

Read the full Forbes article here.

Agentic AI Won’t Scale Without Enterprise Context

Many enterprises are focusing on AI models, infrastructure, and prompt engineering while ignoring the real bottleneck—enterprise context. As a result, most agentic AI initiatives remain stuck in pilots or chatbot use cases, unable to scale into impactful business workflows.

In a Forbes article, Deepak Khosla, the Chief Growth Officer & Head of AI Business at Impetus Technologies, talks about how enterprises that invest in building a strong context foundation will be the ones to move from pilots to scalable, reliable, and cost-efficient agentic AI.