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Anomaly Detection Platform
Tackling business & fraud risks by detecting anomalies & continuous transaction monitoring
A PwC India Product
Anomaly Detection & Transaction Monitoring is a major business problem that requires novel ways for resolution, as patterns keep changing and business rules or supervised models become obsolete quickly. Identifying anomalies through unsupervised ML methods is the key to solving this predicament.
Anomaly Detection Platform is a digital platform that helps clients in detecting and preventing anomalies and continuous transaction monitoring, including uncovering unknown patterns and risks using unsupervised ML methods.
Deployable on cloud or on-premise, the platform is industry agnostic and equipped with a customisable data ingestor, powerful ML engine, natural-language generation (NLG), UI-based business rules engine (BRE), and persona-specific dashboards/workflows, and can be easily configured for multiple use cases.
Challenges
Data barriers
- Data integration challenges due to multiple, siloed sources with different data types and data quality issues, leading to increase in complexity
- Manual checking not able to keep up with rising transaction volumes and makes only sample testing possible, leading to leakages of anomalies
Manual control mechanism
- Turnaround time (TAT) breaches/closure in delays due to manual tracking and lack of audit trails and holistic visibility
- High full-time equivalent (FTE) involvement and costs due to manual processes with more time being spent on identifying anomalies and less on resolving them
Obsolete techniques
- Anomaly patterns keep changing, making static business rules/supervised models obsolete in a short span of time
- Key anomalies not being tagged as validation done only on known risks/patterns with dated predefined rules
Rising compliance cost
- Lack of flexibility in existing monitoring systems to keep pace with the increasing regulatory focus on governance and controls
- Increased compliance cost as audit observations lead to key stakeholder involvement for post-facto remediation
Platform Features
Empower your teams with our one-stop, fully automated platform
Data ingestor
Process any structured and unstructured data through seamless integration with multiple data source types using our built-in API connectors.
Heuristic rules engine with ML module
Tackle both known and unknown risks with our pre-built rules libraries and unsupervised ML models respectively, via a business-friendly user interface (UI) for rules configuration.
Workflow manager
Seize full control of exception management with our customisable workflow manager with real-time user notifications, contextual nudges, audit logs and feedback loops.
Dashboard and insights
Deliver insights via our personalised and action-oriented dashboards aimed at all levels, including analysts and CxOs to drive always-on monitoring of exceptions.
Use Cases
Production
- Machine health - flagging irregular behaviours
- Overall equipment effectiveness (OEE) - drilled down monitoring
- Condition monitoring using power data
Supply chain
- Inventory threshold monitoring at warehouse and store level
Product quality
- Early warning signals - manufacturing quality
- Warranty analytics - fraudulent claims flagging
Finance
- Bank reconciliation
- Account receivable reconciliation (AR)
- Vendor invoice reconciliation (AP)
- GST reconciliation
- Intercompany reconciliation
Sales management
- Distribution/channel partner onboarding
- Spotting bugs in online portals (mispriced items, etc.)
- Early detection of online frauds (coupon misuse, declined transactions, etc.)
- Data quality and assurance
Production
- Early warning signals - manufacturing quality
- Predictive maintenance - flagging irregular behaviours
- Overall equipment effectiveness (OEE) - drilled down monitoring
Sales management
- Channel partner onboarding
- Data quality and assurance
- Detecting sudden surge/decline in online/offline shopping
- Spotting bugs in online portals (mispriced items, etc.)
- Early detection of online frauds (coupon misuse, declined transactions, etc.)
Supply chain
- Inventory reconciliation and threshold monitoring
- Distributor discount - commission model checks
- Fraud risk management
Finance
- Bank reconciliation
- Accounts receivable reconciliation (AR)
- Vendor invoice reconciliation (AP)
- GST reconciliation
Customer management
- Unrelated parties identification check for leads
- Leads and customer (KYC check)
- AML alerts and monitoring
- Data quality and assurance (control checks)
Finance
- GL transaction monitoring and reconciliation
- Account receivable reconciliation (AR)
- Vendor invoice reconciliation (AP)
- Commission variance reconciliation
Operations
- ATM perimeter monitoring
- SWIFT reconciliation
- Capital markets reconciliation
- Investment portfolio monitoring and reconcilation
- Digital payments reconciliation
Audit
- Transaction level checks w.r.t concurrent audit (including rate reasonability, deal confirmation)
- Loans evergreening monitoring
- Branch audit checks
Risk
- Portfolio monitoring
- Monitoring of suspicious transactions at branch level
- Monitoring of suspicious customer deposits
Customer management
- Uncovering application fraud by customers/agents
- Pre-policy issuance checks (incorrect disclosures)
- Leads and customers (KYC and unrelated parties check)
- Data quality and assurance (controls)
Operations
- Persistency monitoring
- AML alerts and monitoring
- Investment portfolio monitoring and reconciliation
Finance
- General ledger reconciliation
- Inter-company reconciliation
- Premium collection reconciliation
- Commission payout reconciliation
- Accounts receivable reconciliation (AR)
- Accounts payable reconciliation (AP)
Claims management
- Medical service provider collusion - billing checks
- Padded claims detection for motor insurance
- Organised fraud, including staged collisions
- Policyholder profile, claims history, and location-based triggers
- Subrogation case detection
Underwriting
- Predictive risk scoring for intelligent underwriting
- Risk assessment using wearables data
- Enhanced underwriting via cognitive capture
Customer management
- Uncovering application fraud by customers/agents
- Pre-policy issuance checks (incorrect disclosures)
- Leads and customers (KYC and unrelated parties check)
- Data quality and assurance (controls)
Finance
- General ledger reconciliation
- Inter-company reconciliation
- Premium collection reconciliation
- Commission payout reconciliation
- Accounts receivable reconciliation (AR)
- Accounts payable reconciliation (AP)
Underwriting
- Predictive risk scoring for intelligent underwriting
- Risk assessment using wearables data
- Enhanced underwriting via cognitive capture
Operations
- Persistency monitoring
- AML alerts and monitoring
- Investment portfolio monitoring and reconciliation
Claims management
- Payout optimisation and settlement
- Fraud detection using predictive analytics
- Litigation prevention via text analytics
- Fraud detection using predictive analytics
How does the Anomaly Detection Platform help you?
Streamlined processes, proactive remediation and seamless compliance
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A platform with a difference
Indicative Commercial Model
Rates will be tailored as per local territory.
Implementation fee unless specified would be additional.
Any customisation to the standard offering will be charged on a case by case basis.
License Fee &
Platform Setup
INR 15 Lacs*
One time
Platform deployment and enablement for use
Use-case
Implementations
INR 15 - 20 Lacs*
Per use case
- 5 to 10 sources to be integrated
- 2 to 20 business roles
- 1 to 3 ML scenarios to be enabled
- 1 to 10 workflow alerts
- 5 to 10 reports and dashboards
Operating Cost
(optional)
INR 2.5 Lacs*
Per person, monthly
- Maintenance Support (User query support and platform
- availability) based on the number of persons
- Client Infrastructure Cost considered to be part of TCO
CR & Support
Management (as per
the need)
INR 5 - 20 Lacs*
Per feature release
- Output Integration to Core Systems
- Outbound feeds to downstream app
- SSO Integration
- Item addition to existing features
- Based on tool set, number of ETL pipelines etc
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