Risk & Fraud Analytics

Risk & Fraud Analytics

Fraud – and financial crime in general – is one of the biggest risks that many organizations face. Increasingly agile fraud perpetrators have benefited from firms’ limited ability to adapt.

ITC Infotech
TCG Digital

Fraud – and financial crime in general – is one of the biggest risks that many organizations face. Increasingly agile fraud perpetrators have benefited from firms’ limited ability to adapt. While most institutions have well-funded anti-fraud groups, key resources are often fragmented across the organization. Essential data, investigative and forensics expertise, and analytics talent are typically distributed across cyber, compliance, legal, IT, and fraud teams, with little to no coordination or data sharing.

Our Risk & Fraud solutions analyze vast amounts of mobile, application, and transaction data in real time to detect known and emerging fraud. We could deliver data-driven insights to help your enterprise or organization detect, report and prevent threats.

  • Value: Reduced risk exposure, reduced default.

    Credit Risk is the risk of default on a debt that may arise from a borrower failing to make required payments. Lending money is a business fraught with risks and there is no guarantee that you will get all your money back. If the counterparty defaults or if their credit quality deteriorates, the loan will become riskier.

    Credit Risk Analysis helps mitigate business loss by understanding a bank’s capital adequacy and loan loss reserves at any given time. Effective Credit Risk Analytics  & Modeling can not only help your business meet regulatory requirements but also anticipate any disruptions to cash flows.

    Our experts can help with:

    • Credit scoring
    • Development, validation, calibration and implementation of Probability of Default (PD), Loss Given Default (LGD), Value at Risk (VaR) estimation & forecasting, Low Default Portfolio (LDP) and Non –LDP portfolios.
    • Correlation modeling & estimation, validation, implementation of prudential regulation.
    • Portfolio Risk Management including modeling & validation of key risk drivers for loss & other credit risk analysis measures.
    • Stress testing of existing credit risk analysis modeling concepts.

  • Value: Reduced risk exposure, better risk matrices

    Market Risk Analytics is aimed at mitigating risk by offering techniques such as Value at Risk (VaR) Assessment, Scenario Analysis, Stress Testing, Correlation Analysis, Volatility Correction etc.

    Market risk, also called “systemic risk, is the possibility for an investor to experience losses due to factors that affect the overall performance of the financial markets.

    Meet risk and regulatory compliance challenges head on with market risk analysis that is predictive, adaptive, integrated and useful. Keep up with the rapidly changing regulatory environment by creating scenarios to be always ready for future potential exposures.

    Our experts can help with:

    • Asset Liability Management by providing key liquidity risk ratios, assessing your portfolio liquidity situa­tion and liquidity hedging strategy.
    • Assessing fund transfer pricing with or without risk-based spreads (e.g., credit, liquidity and option-adjusted spreads), and calculate economic value.
    • Advanced analysis across risk types, including stress testing and modeling liquidity risk, net interest income and economic value.
    • Review risk management structure and policies
    • Risk Modeling and Reporting including designing reporting templates and workflows.
    • Risk-Based Decision Making including designing risk-based Pricing Framework and Portfolio Strategy.

  • Value: Arrest claim fraud, reduce pay-out costs

    Risk management has always been an integral part of the insurance business. Firms succeed through their ability to identify and manage risks facing their clients. Successful insurers not only understand risk clearly, but also dynamically use available insights to handle exposure to risk through measures aimed at avoiding or preventing losses and screening, pre-empting and pricing them-in in the underwriting process.

    Fraudulent claims are a serious financial burden and impact the financial health not only of insurers, but also of innocent people seeking effective insurance coverage. Predictive analytics can help in insurance fraud detection by uncovering the likeliness of frauds arising in medical billing, life insurance, claims and other areas of the business.

    Data scientists today can leverage complex and sophisticated capabilities such as predictive modeling, text mining, database searches, anomaly detection and network link analysis to create an advanced and powerful fraud analytics engine.

    Our experts can help check claims fraud by:

    • Identifying duplicate claims.
    • Flagging up suspicious transactions for a detailed follow-up.
    • Decrease insurance fraud losses by detecting and preventing fraud before claims are paid leveraging an advanced fraud modeling engine.
    • Persistently improving historical models to address changes in insurance fraud trends.
    • Determining dealers/agents with a high numbers of claim payouts.
    • Attaining fewer false positives that translate into greater customer satisfaction.

  • Value: Reduced risk exposure, better risk matrices.

    Fraud in banking has the potential to cause serious business loss. Fraudulent activity can arise from bank employees and customers alike. Analytical insights from transaction data, employee and other sources can help identify fraud in billing, cash and cheque transactions.

    Banks are constantly faced with challenges related to balancing opportunity and risk while adhering to increasing regulatory demands. Multiple advanced techniques for fraud analytics in banking provide the foundation for strong fraud prevention programs by analyzing transactions and customer activity, developing new models and fine-tuning existing ones to improve fraud detection efficiency.

    Our experts can help with:

    • Leveraging multiple analytic techniques to flag suspicious activities for review.
    • Risk Scoring of all kinds of transactions to analyze payment behavior.
    • Detecting abnormal patterns that may indicate previously unknown fraud.
    • Predictive modeling to uncover new fraud based on previous fraud profiles.
    • Advanced statistical modeling, deterministic and associative analysis of multiple parties to identify repeat offenders and mitigate fraud in its tracks, before it occurs.
    • Quarantining suspicious transactions such as cash movements just under regulatory reporting thresholds or large number of cash transfers by Customer Id and account.

  • Value: Reduced procurement costs

    Fraud in procurement has existed for centuries and organizations of all sizes and across industries grapple with serious irregularities. While the risk can never be fully eliminated, a procurement analyst or a company can enlist help to leverage advanced analytics to implement controls that limit the likelihood of these occurring.

    Procurement fraud stretches across different types of activities, such as falsification or magnification of a need for a product or service, an RFP drafted to favor a vendor, splitting the purchase to fall within thresholds, favoritism by issue a contract on a party nomination basis, There could be instances of collusion with procurement functions in ways that compromise the fairness of the procurement process resulting in higher contract costs.

    A procurement analyst can lean on advanced analytics techniques such as Data Mining, Text Mining, Anomaly Detection, and Network Analysis to arrest or reduce the incidence of this damaging flavor of fraud.

    Our experts can help with:

    • Flagging suspicious transactions that might go undetected.
    • Determining the possibility of split purchase orders, duplicate payments etc.
    • Indentifying possible buyer-vendor collusions.
    • Addressing inefficiencies in the procurement process by identifying differential pricing for similar items.
    • Leveraging predictive modeling to identify attributes or patterns that are correlated with known fraud.