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Industry Sector : Government & Public Sector

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Risk Management:

Mapping operational, financial, compliance, and reputational risks distinctly, considering both isolated and interlinked impacts.

Using historical data, incident reports, and predictive analytics to assess likelihood and impact for each risk type.

Implementing scoring systems at citizen, entity, or transaction levels to flag high-risk profiles proactively.

Designing risk-specific interventions—e.g., compliance tightening, process audits, outreach programs—to minimize exposure without affecting service delivery.

Embedding real-time risk scores into government CRM, welfare, or enforcement systems for dynamic decision-making and early containment.

Establishing continuous feedback loops to recalibrate risk models based on new data, emerging threats, and regulatory changes.

Facilitating structured data sharing across departments to detect systemic risks and prevent siloed risk oversight.

Our Market Ready Solution

01 Customer Risk Scorecarding

Customer Risk Scorecarding :

Defining Risk Objectives by Scheme or Service: Mapping out specific risk outcomes (e.g., subsidy misuse, default, policy violation) aligned with government schemes. Building Individual-Level Risk Profiles: Using past behavior (e.g., repayment history, claims pattern) and dynamic variables like individual debt burden or income volatility to score citizens or entities. Deploying Predictive Models: Applying logit, tobit, or gamma regression to develop risk models; tailor methodology to the nature of the scheme and available data. Segmenting by Risk Tier: Classify citizens or service users into risk categories (low, moderate, high) for prioritized intervention and policy targeting. Integrating with Government CRM/Portals: Embed risk scores into e-governance platforms to trigger alerts, automate document verification, or flag cases needing deeper scrutiny. Designing Segment-Specific Mitigation Protocols : Create differentiated engagement—supportive outreach for low-risk, AI-based verification for moderate, and field investigation for high-risk users. Creating Feedback Loops: Continuously refine models using new behavioral data, fraud flags, or benefit claim audits to improve accuracy and relevance.
02 False Alarm Management

False Alarm Management :

Quantifying Type 1 & Type 2 Risk Costs : Measure cost of false positives (delayed citizen services) vs. false negatives (missed fraud or compliance breaches) for each scheme or process. Developing Risk Threshold Optimization Models : Build AI/ML models to balance scrutiny vs. facilitation, optimizing risk thresholds by scheme value, fraud exposure, and service impact. Deploying Decision Support Systems : Integrate adaptive rule engines or scoring layers into service portals to dynamically adjust scrutiny based on transaction context and past patterns. Segmenting Transactions by Value & Risk Sensitivity: Apply stricter filters on high-value or compliance-sensitive transactions; ease checks on verified low-risk profiles to improve citizen experience. Monitoring False Alarm Ratios Continuously : Use real-time dashboards to track alert accuracy, update thresholds, and minimize alert fatigue for case officers or auditors. Establish Audit-Integrated Feedback Loops : Feed audit outcomes and investigation data back into the model to improve risk signal calibration over time.
03 Fraud Detection & Forensics

Fraud Detection & Forensics :

Mapping High-Risk Fraud Scenarios : Identify prevalent fraud types (e.g., misdeclaration, misvaluation, benefit manipulation) relevant to the department’s schemes, subsidies, or procurement systems. Deploying Pattern Recognition Models : Use neural networks and anomaly detection to flag suspicious applications, transactions, or claims—especially where gains are possible with minimal misrepresentation. Leveraging Historical & External Data: Combine department data with external sources (e.g., PAN, GSTN, property registries) to enrich risk profiling and detect concealed ownership or misreporting. Implementing Real-Time Screening at Entry Points: Integrate fraud scoring engines at the application or disbursal stage to enable pre-sanction alerts and reduce post-facto recovery effort. Designing Permutation-Rule Engines : Develop custom rule-based systems to flag rare combinations or improbable declarations across schemes or departments. Creating Investigative Feedback Loops : Feed findings from vigilance, audit, and inspection back into models to improve risk signal accuracy and evolve detection logic. Enabling Department-Wide Visibility : Build a unified fraud analytics dashboard accessible to authorized officials across field offices, enforcement, and policy units.
04 Entity 360 Risk Profiling

Entity 360 Risk Profiling:

Mapping Multi-Entity Transaction Networks: Identify and digitally map all entities involved in high-risk transactions to detect concealed interlinkages or collusion potential. Quantifying Historical Risk Contribution: Use graph analytics and machine learning to compute each entity’s past involvement in flagged or fraudulent transactions, weighted by severity and frequency. Detecting Liaison Risk Patterns : Apply network-based algorithms to uncover risky interactions across entities—revealing indirect relationships that may contribute to fraud or manipulation. Scoring Entities Using Composite Risk Index : Develop a composite risk score combining individual behavior, transaction context, and co-occurrence patterns—enabling precise entity-level risk profiling. Tagging Future Transactions in Real Time : Integrate risk scores into decision systems to auto-flag transactions involving high-risk entities or suspicious linkages for pre-approval review. Enabling Cross-Agency Risk Intelligence : Share entity profiles and risk tags across departments to ensure coordinated oversight and prevent regulatory arbitrage.
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