Accurate daily liquidity and cash balance projections
Improved cash management and funding decisions
Reduced manual data preparation and errors
Real-time, actionable insights for treasury teams
Scalable and auditable forecasting framework
Tax
Analytics
SIFT’s solution for tax analytics automates the aggregation, cleansing, and standardization of tax data from multiple jurisdictions into a unified workflow. Advanced analytics and built-in validation rules ensure accurate calculations, compliance with regulatory requirements, and full auditability. The framework accelerates reporting cycles, reduces manual errors, and provides actionable insights for tax planning and risk management.
Accurate, compliant, and audit-ready tax reports
Reduced cycle time and manual effort
Minimized risk of errors and regulatory penalties
Enhanced visibility across jurisdictions
Actionable insights for tax planning and decision-making
Month-End
Close
SIFT’s solution for month-end close automates the preparation of journal entries, data consolidation, and variance analysis across financial systems. Standardized workflows ensure accuracy, reduce manual effort, and enable faster close cycles. The framework provides audit-ready outputs and real-time visibility into financial performance.
Faster, more accurate month-end close
Reduced manual effort and errors
Automated variance analysis and reconciliation
Transparent, audit-ready financial workflows
Improved visibility into financial performance
Regulatory Reporting
SIFT’s solution for regulatory reporting automates the aggregation, validation, and transformation of data to generate CCAR, Basel, and DFAST-compliant reports. Governed workflows ensure traceability, accuracy, and adherence to regulatory standards, while automation reduces manual effort and supports timely submissions.
Accurate, compliant, and audit-ready regulatory reports
Reduced manual effort and reporting errors
Full traceability and governance across data workflows
Faster reporting cycles with reliable outputs
Scalable framework adaptable to evolving regulatory requirements
General Ledger Reconciliation
SIFT’s solution for general ledger reconciliation automates the matching, validation, and exception handling of financial transactions across systems. Standardized workflows reduce reliance on manual Excel processes, ensure accuracy, and provide audit-ready records.
Faster and more accurate ledger reconciliations
Reduced manual effort and errors
Automated exception identification and resolution
Transparent, audit-ready workflows
Treasury Forecasting
SIFT’s solution for treasury forecasting integrates real-time data from treasury and financial systems to project daily liquidity and cash balances. Automated workflows cleanse, consolidate, and validate inputs, while predictive models generate accurate forecasts to support decision-making and cash management.
Accurate daily liquidity and cash balance projections
Improved cash management and funding decisions
Reduced manual data preparation and errors
Real-time, actionable insights for treasury teams
Scalable and auditable forecasting framework
Credit Risk Scoring and Monitoring
SIFT implements an automated data pipeline that ingests structured and unstructured financial data from multiple internal and external sources, applies data cleansing and normalization techniques, and then leverages advanced scoring models with machine learning-driven risk segmentation. Real-time monitoring dashboards are built with rule-based triggers to continuously track credit exposures across customers, portfolios, and counterparties.
More precise view of default risk and exposures
Dashboards detect credit deterioration quickly
Auditable data lineage ensures regulatory alignment
Liquidity & Capital Stress Testing
SIFT implements a scenario-based simulation framework that integrates historical and real-time financial data to model adverse market conditions. Advanced stress-testing algorithms project capital adequacy and liquidity coverage under regulatory requirements, while automated dashboards provide continuous monitoring and exception reporting.
Identify potential liquidity or capital shortfalls before they materialize
Ensure alignment with Basel, CCAR, and other regulatory standards
Generate actionable reports for strategic decision-making
Operational Risk Event Analysis
SIFT implements an automated operational risk analytics framework that aggregates loss event data from multiple sources, cleanses and standardizes it, and applies advanced pattern recognition and root-cause analysis algorithms. Interactive dashboards highlight trends, anomalies, and risk hotspots for timely investigation.
Quickly identify recurring issues and high-risk areas
Automation reduces manual effort in analyzing loss events
Insights support targeted actions to prevent future losses
Model Risk Validation
SIFT implements an automated model validation framework that continuously tests and monitors model performance using historical and real-time data. Advanced analytics detect deviations, bias, and model drift, while dashboards provide governance, transparency, and compliance with SR 11-7 guidelines.
Continuous validation ensures models perform as intended
Supports SR 11-7 standards with auditable validation records
Transparent monitoring enables better risk oversight and decision-making
Regulatory Risk Reporting
SIFT implements an automated regulatory reporting framework that consolidates data from multiple sources, applies validation and reconciliation rules, and generates Basel III, CCAR, and ICAAP reports with high accuracy. Dashboards and alerts ensure timely submission and audit readiness.
Automated data validation reduces errors in regulatory reports
Less manual effort speeds up report generation and submission
Transparent processes ensure adherence to regulatory standards
Third-Party Risk & Vendor Management
SIFT implements an automated third-party risk management framework that streamlines vendor onboarding, risk scoring, and continuous monitoring. Integrated dashboards track vendor performance, compliance, and potential operational risks, enabling proactive mitigation.
Early identification of high-risk vendors minimizes operational disruptions
Automated onboarding and scoring save time and resources
Continuous monitoring ensures adherence to regulatory and internal standards
Portfolio Risk & Performance
SIFT implements a real-time portfolio risk and performance analytics framework that integrates data across asset classes. Advanced algorithms calculate exposure, returns, and volatility, while interactive dashboards provide insights for risk-adjusted decision-making.
Clear view of exposure, performance, and volatility
Real-time analytics enable proactive portfolio adjustments
Supports strategies to maximize returns while managing risk
Client Segmentation & Personalization
SIFT implements an advanced client segmentation and personalization framework that analyzes behavioral data, wealth tiers, and investment preferences. Machine learning models identify patterns and clusters, enabling targeted marketing and customized offerings.
Understand client behavior and preferences more accurately
Deliver tailored products and services to increase engagement
Targeted strategies drive higher conversion and loyalty
Fund Flow Forecasting & Reporting
SIFT implements a fund flow forecasting and reporting framework that integrates historical and real-time transaction data to predict inflows and outflows. Automated reporting tools generate timely investor reports and dashboards, enhancing transparency and operational planning.
Predict fund movements for better liquidity management
Automated reporting reduces manual effort and errors
Clear insights for investors and stakeholders improve trust
ESG Scoring
SIFT implements an ESG scoring framework that integrates environmental, social, and governance metrics with portfolio holdings data. Advanced analytics calculate ESG scores, track performance trends, and generate automated client reports to support responsible investing.
ESG scores provide insights for responsible investment decisions
Automated reports improve transparency and engagement
Continuous monitoring of ESG metrics supports long-term sustainability goals
Investment Proposal Automation
SIFT implements an investment proposal automation framework that integrates client data, risk profiles, and investment preferences to generate personalized proposals. Automated workflows ensure consistency, compliance, and rapid delivery to clients.
Reduce turnaround time for personalized proposals
Standardized templates ensure regulatory adherence
Tailored proposals improve engagement and satisfaction
Fee Leakage & Billing Reconciliation
SIFT implements an automated fee leakage and billing reconciliation framework that aggregates transaction and billing data, identifies missing or misapplied fees, and validates invoices against contractual agreements. Dashboards highlight discrepancies and generate actionable reports for recovery.
Detect and correct missing or misapplied fees promptly
Automated validation reduces manual errors in billing
Streamlined processes save time and resources
Trade Reconciliation & Exception Management
SIFT implements an automated trade reconciliation and exception management framework that consolidates trade data across multiple systems, matches transactions, and flags discrepancies. Advanced workflows handle exceptions efficiently, reducing manual intervention and accelerating settlements.
Automated matching minimizes discrepancies
Streamlined exception handling accelerates
transaction completion
Less manual effort and improved accuracy in trade processing
Pre- and Post-Trade Analytics
SIFT implements a pre- and post-trade analytics framework that collects and analyzes trade execution data, measuring slippage, market impact, and execution quality. Dashboards and reports provide insights to optimize trading strategies and evaluate broker performance.
Data-driven insights improve execution decisions
Monitor and compare broker performance effectively
Identify and mitigate factors affecting trade efficiency
Transaction Cost Analysis
SIFT implements a transaction cost analysis framework that aggregates trading data across channels and instruments, calculates explicit and implicit costs, and identifies cost drivers. Automated dashboards provide insights to optimize execution decisions and reduce trading drag.
Identify and mitigate cost drivers to improve profitability
Data-driven insights enhance trading strategy effectiveness
Clear reporting across channels and instruments supports better oversight
Algorithmic Strategy Backtesting
SIFT implements an algorithmic strategy backtesting framework that uses historical and simulated market data to evaluate trading strategies. Advanced analytics measure performance, risk, and execution metrics, while dashboards provide insights for refinement and optimization.
Identify strengths and weaknesses to improve trading algorithms
Evaluate potential losses under different market conditions before live execution
Insights support informed adjustments to enhance performance
Market Surveillance and Compliance
SIFT implements a market surveillance and compliance framework that continuously monitors trading activity, applies anomaly detection algorithms, and automatically generates alerts for suspicious patterns. Dashboards provide oversight for regulatory and internal compliance teams.
Identify unusual trading patterns promptly
Automated alerts support adherence to internal and external regulations
Reduce manual monitoring efforts while maintaining robust oversight
Intraday Risk & Exposure Monitoring
SIFT implements an intraday risk and exposure monitoring framework that collects real-time position, margin, and exposure data across portfolios. Automated dashboards and alerts provide continuous visibility, enabling proactive risk management during trading hours.
Continuous monitoring of positions and exposures
Early detection of potential issues allows timely intervention
Data-driven insights support intraday trading and risk strategies
Claims
Forecasting
SIFT consolidates historical claims data from multiple sources, ensures its accuracy through cleansing and validation, applies advanced analytics and predictive modeling to uncover trends and forecast future claims, and provides interactive dashboards for real-time monitoring and strategic planning.
More accurate claims forecasts, enabling better allocation of budget reserves
Reduced risk of over- or under-reserving
Enhanced operational planning and resource optimization
Fraud Detection & Prevention
SIFT automates fraud detection by consolidating claims and behavioral data from multiple sources, cleansing and standardizing it for consistency, and applying anomaly detection and predictive modeling techniques to flag suspicious patterns in real time. Automated workflows route flagged claims for investigation, while dashboards provide visibility into fraud trends and model performance.
Detects fraudulent claims faster and more accurately
Reduces financial losses and unnecessary payouts
Strengthens compliance and fraud prevention controls
Claims Processing Optimization
SIFT automates claims handling by integrating data from multiple systems, cleansing and validating inputs for accuracy, and streamlining workflows to reduce repetitive manual tasks. Dashboards provide visibility into processing times, exceptions, and overall performance, enabling proactive improvements.
Speeds up claims handling and improves customer experience
Reduces manual workload and error rates
Enhances operational efficiency and transparency
Payment Integrity Analysis
SIFT automates payment integrity analysis by consolidating claim data across sources, applying validation rules and anomaly detection to flag irregularities, and highlighting potential overpayments or duplicate claims. Automated workflows surface recovery opportunities while dashboards track financial impact and resolution progress.
Detects overpayments and billing errors quickly
Identifies recovery opportunities to reduce financial leakage
Improves payment accuracy and overall claims integrity
Historical Claims Trend Analysis
SIFT automates payment integrity analysis by consolidating claim data across sources, applying validation rules and anomaly detection to flag irregularities, and highlighting potential overpayments or duplicate claims. Automated workflows surface recovery opportunities while dashboards track financial impact and resolution progress.
Detects overpayments and billing errors quickly
Identifies recovery opportunities to reduce financial leakage
Improves payment accuracy and overall claims integrity
Rate Development Acceleration
SIFT blends loss experience with market data to accelerate pricing model iterations, enabling faster and more accurate rate development while integrating insights into underwriting decisions.
Speeds up pricing model updates and decision-making
Improves rate accuracy and competitiveness
Enhances overall profitability and risk management
Risk Adjustment
Forecasting
SIFT’s solution streamlines risk adjustment forecasting by unifying claims, clinical, and demographic data into a single workflow. Data is cleansed, standardized, and enriched for consistency before advanced models identify patterns in historical claims and patient profiles. Machine learning refines forecasts, while validation ensures transparency and compliance. The framework enables dynamic updates and delivers results seamlessly into dashboards and reporting systems.
Speeds up pricing model updates and decision-making
More accurate risk score predictions for better reimbursement alignment
Early identification of high-risk members for proactive care
Greater efficiency through automation of manual tasks
Cash Flow & Liability Forecasting
SIFT analyzes millions of policy records to project future claims and optimize capital reserves, combining historical trends, loss patterns, and predictive analytics for accurate forecasting.
Improves accuracy of claims and liability projections
Optimizes capital allocation and reserve planning
Enhances financial stability and decision-making
INCR Reserve Modeling Automation
SIFT replaces manual SQL or Excel processes with automated reserve modeling workflows that are dynamic, scalable, and auditable, enabling faster and more accurate reserve estimates
Speeds up reserve calculations and reporting cycles
Improves accuracy, consistency, and auditability
Enhances efficiency by reducing manual effort and errors
Financial Risk Modeling Enablement
SIFT empowers actuaries to quickly build and refine financial risk models by connecting diverse data sources and automating workflows, enabling more agile and accurate modeling.
✅ Accelerates model development and iteration cycles
✅ Improves accuracy and reliability of financial risk insights
✅ Enhances agility in responding to market and regulatory changes
Underwriting Risk Scoring Automation
SIFT automates underwriting by generating accurate risk scores from behavioral and historical data. Data is consolidated, cleaned, and enriched, then predictive models calculate risk scores and assign categories. Automated workflows integrate scoring into underwriting systems, while dashboards track trends and performance.
Improved risk differentiation and scoring accuracy
Proactive risk management through continuous model updates
Submission Triage & Prioritization
SIFT automates intake and routing of broker submissions, consolidates and standardizes data, and applies predictive models to prioritize high-value or high-risk opportunities, while dashboards provide real-time visibility.
Enables faster processing, reduces backlog, improves focus on high-value submissions
Enhances resource allocation and underwriting efficiency, and supports data-driven decision-making
Pricing & Rate Adequacy Monitoring
SIFT analyzes quoted versus bound premium trends and loss experience, consolidates historical and current data, and applies analytics to identify underpriced or high-risk segments, with dashboards providing clear insights for pricing adjustments.
Enables timely detection of underpriced segments
Improves rate adequacy enhances profitability efficiency, and supports data-driven decision-making
Supports data-driven pricing decisions, and strengthens risk management
Broker Performance & Hit Ratio Analytics
SIFT tracks submission-to-bind ratios and outcomes by broker or channel, consolidates historical and current data, and applies analytics to evaluate broker performance and optimize underwriting strategies.
Identifies top-performing brokers and channels for targeted engagement
Improves underwriting strategy and resource allocation
Increases overall hit ratios and business efficiency
Real-time Eligibility & Rule Checking
SIFT automates intake and routing of broker submissions, consolidates and standardizes data, and applies predictive models to prioritize high-value or high-risk opportunities, while dashboards provide real-time visibility.
Enables faster processing, reduces backlog, improves focus on high-value submissions
Enhances resource allocation and underwriting efficiency, and supports data-driven decision-making.
Regulatory Reporting
SIFT’s solution for regulatory reporting automates the collection, validation, and consolidation of data from multiple sources into a unified workflow. Data is standardized and transformed to meet compliance requirements, with full traceability and auditability built in. The framework reduces manual errors, supports rapid adaptation to changing regulations, and delivers outputs in ready-to-submit formats that integrate seamlessly with dashboards for monitoring and governance.
Accurate, compliant, and audit-ready reports
Significant time savings through automation of repetitive tasks
Reduced operational and compliance risk from manual errors
Scalable solution that adapts to changing regulatory requirements
Risk Adjustment Reporting
SIFT’s solution for risk adjustment reporting unifies claims, clinical, and demographic data into a standardized workflow to generate accurate, compliant, and audit-ready outputs. Automated data cleansing, validation, and transformation ensure consistency, while embedded logic applies regulatory rules for risk score calculations. The process reduces manual effort, minimizes errors, and provides full traceability, enabling transparent audits and quick adaptation to evolving reporting requirements.
Accurate and compliant risk adjustment reports
Reduced manual effort and error rates through automation
Faster turnaround for reporting cycles
Scalable framework adaptable to new regulations and data sources
Reinsurance Treaty Processing
SIFT’s solution for reinsurance treaty processing automates ingestion and standardization of policy, claims, and treaty data. Parameterized rules handle ceding, retention, and recoverables, while automated reconciliation validates results with exception handling. Real-time updates and embedded audit trails ensure accuracy, transparency, and compliance, with outputs ready for finance and regulatory reporting.
Faster and more accurate treaty calculations and settlements
Reduced operational risk through automated data validation
Increased efficiency by eliminating manual reconciliation tasks
Scalable framework adaptable to complex treaty structures and evolving requirements
Fraud Detection & Risk Analytics
SIFT tracks submission-to-bind ratios and outcomes by broker or channel, consolidates historical and current data, and applies analytics to evaluate broker performance and optimize underwriting strategies.
Identifies top-performing brokers and channels for targeted engagement
Improves underwriting strategy and resource allocation
Increases overall hit ratios and business efficiency
Exposure & Risk Monitoring
SIFT automates intake and routing of broker submissions, consolidates and standardizes data, and applies predictive models to prioritize high-value or high-risk opportunities, while dashboards provide real-time visibility.
Enables faster processing, reduces backlog, and improves focus on high-value submissions
Enhances resource allocation and underwriting efficiency, and supports data-driven decision-making.
Readmission Risk Prediction
SIFT’s leverages predictive modeling to identify patients at high risk of 30-day hospital readmissions. Patient data, including clinical, demographic, and historical admission records, is cleansed, standardized, and integrated into the workflow. Machine learning algorithms analyze patterns and risk factors to generate accurate, actionable risk scores. The framework enables proactive interventions, continuous model refinement, and full auditability, ensuring both clinical effectiveness and compliance with CMS guidelines.
Early identification of high-risk patients
Reduced 30-day readmissions and CMS penalties
Data-driven, actionable insights for care teams
Continuous model improvement with new data
Drug Utilization Analysis
SIFT’s solution for drug utilization analysis automates the collection, cleansing, and integration of prescription, claims, and cost data into a unified workflow. Advanced analytics identify patterns in medication usage, spending trends, and potential inefficiencies, while built-in rules flag anomalies or high-cost outliers. The framework supports dynamic reporting and audit-ready insights to optimize pharmacy benefit management and decision-making.
Clear visibility into medication usage and costs
Identification of high-cost drugs and utilization trends
Automated, error-reducing reporting
Data-driven support for pharmacy benefit management
Length of Stay Forecasting
SIFT’s solution for length of stay forecasting integrates patient, clinical, and historical admission data into a unified workflow. Advanced predictive models analyze patterns and risk factors to estimate inpatient stay duration, enabling optimized discharge planning and bed management. Automated validation ensures accuracy, while outputs are delivered in actionable formats for clinical and operational teams.
Accurate predictions of inpatient length of stay
Improved discharge planning and bed utilization
Enhanced operational efficiency in hospital management
Reduced risk of overcrowding and delays
Care Gap
Analysis
SIFT’s solution for care gap analysis automates the integration and cleansing of clinical, claims, and demographic data to provide patient-level insights. Predictive and rule-based analytics identify missed preventative care opportunities, enabling care teams to prioritize interventions. The framework ensures data consistency, auditability, and delivers actionable insights directly to clinical workflows.
Identification of patients with unmet preventative care needs
Actionable insights for targeted interventions
Automated data pipelines reducing manual effort
Improved patient outcomes and care quality
Clinical Quality Reporting
SIFT’s solution for clinical quality reporting automates the aggregation, cleansing, and standardization of clinical and claims data into a unified workflow. Built-in logic aligns metrics with regulatory and value-based care standards, while automated reporting reduces manual effort and ensures accuracy. The framework provides audit-ready outputs and supports continuous monitoring of clinical performance and outcomes.
Accurate, compliant, and timely clinical quality reports
Reduced manual reporting effort and errors
Real-time tracking of performance metrics and outcomes
Support for value-based care and regulatory compliance
Transparent, audit-ready workflows for governance and decision-making