Regulator Reporting Metric
Internal Reporting Metric
Internal Governance Metric
External Governance Metric
Regulatory Disclosures
Non Regulatory Disclosure
Portfolio Quality Analytic
Portfolio Risk Analytics
Regressive Risk Analytics
Parametric VaR Analytics
Capital Allocation Analytics
Risk Tolerance Analytics
Neural Risk Analytics
Simulated VaR Analytics
Risk Appetite Analytics
Stress Testing Analytics
Credit Risk
Market Risk
Operational Risk
Liquidity Risk
IFRS 9
Model Validation
ICAAP Modeling
ILAAP Modeling
Post 2008 financial crisis, regulators around the world realized that history based accounting standards can be extremely misleading and a paradigm shift in accounting methodologies is needed.
In July 2014, IASB issued IFRS 9 standard which replaced existing IAS 39 provisioning method. The IASB stated that the IAS 39 incurred cost method leads to delayed recognition of credit losses, thus a forward looking approach is being introduced.
IFRS 9 became effective worldwide on Jan 2018 and has since then posed huge challenges for accounting firms, auditors and corporations who were neither used to deal with such predictive analytics nor qualified to do so.
Incurred Loss Provisioning Method under IAS 39 has been replaced by Expected Loss Provisioning Approach of IFRS 9 where provisions are taken upfront and Expected Credit Loss (ECL) is estimated as a product of EAD, PD and LGD.
Definition of EAD is different for each of 3 buckets, term structure of applicable point in time PD is also different for each of 3 buckets of EAD, and LGD is estimated differently for collateralized and non collateralized exposures.
Segmentation & pooling of portfolio on the basis of discriminant analysis and statistical mass.
Identification of statistically significant macro-economic variables for each segment’s default rates.
Projections of macro-economic scenarios and assignment of probabilistic weight to each scenario.
Distribution of current exposure under each segment into three days past due (DPD) buckets as defined by IFRS 9.
Estimation of point in time probability of default term structure for each segment of portfolio exposure.
Estimation of point in time loss given default distribution for each segment of portfolio exposure.
Receivables’ portfolio is segmented on the basis of both empirical judgement as well as statistical characteristics estimated through statistical moments of default rates’ pattern.
Receivables from counterparties based in different countries must be segmented separately as their default rates will be correlated to the macro-economic parameters of their domiciled countries.
Segments having similar range of statistical moments i.e. mean, standard deviation, skewness and kurtosis are merged.
Historical data of maximum number of relevant macro-economic variables is taken and tested for Correlation & Concordance with portfolios’ historical default rates.
Macro-economic variables that qualify minimum thresholds of correlation & concordance are taken for while other non-qualifying variables are discarded.
Qualified macro-economic variables are further tested for R-Square (Model Fit) and P-Values (Predictive Capability) for default rates pattern through regressive analytics.
Macro-economic variables that qualify minimum thresholds of model fit and predictive capability are taken while other variables are statistically rejected.
Identified macro-economic variables are empirically back tested to ascertain statistical validity.
This step is conducted for each segment of receivables’ default rates as identified in portfolio segmentation process.
Annual projected data for final MEVs is taken and YOY Intercepts and Slopes of annual data points are estimated. Annual data points are converted in quarterly data points through Interpolation using estimated YOY intercepts and slopes and YOY rate of changes is estimated for projected MEVs to Stationarize data.
Stationarized MEVs projected data is combined with historical standardized data points to create data stream. Factor Analysis is conducted on data stream to estimate Eigenvalues & Eigenvectors and Principal Components are identified using Eigenvalues & Eigenvectors through Principal Component Analysis.
Primary Principal Component is identified on the basis of Explained Cumulative Variance and Regressive Analytics are conducted to test Statistical Fit & Predictive Accuracy of DRs using Principal Components.
Base, Down and Up scenarios are identified by mapping movements of DR with the increase in Principal Component using Visual Cutoff Points on scree plot. Probabilistic weights are estimated for each scenario as Realized Probabilities based on Cutoff Points adjustments in historic default rate pattern.
Regressive Analytics are conducted to test Statistical Fit & Predictive Accuracy of DRs under base, down and up scenarios
Slopes & Intercepts of base, down and up scenarios are used to project DR changes under 3 scenarios (IFRS 9 requires minimum of 2 scenarios).
The process of point in time probability of default requires combining non-parametric approach with parametric approach under IFRS 9 standard modeling.
Net Flow Rates are estimated from historical default rate pattern under non-parametric approach and adjusted for its standard deviation.
Economic overlay is created on historic net flow rates by combining macro-economic scenarios through exponential distribution which estimates point forward probabilities of default under each scenario.
Final point in time probabilities of default for each segment of receivables portfolio are estimated as weighted average of scenario’s probabilistic weight and scenario driven probability of default.
This is essentially a copula approach which substitutes transition matrix which is extremely data intensive otherwise.
PIT collateral values are estimated by extrapolating the negative/positive Slope of valuation points over Euclidean Space for collateralized exposures using estimated accelerators.
Segment-wise total outstanding exposure and total collateral values are used to calculate Exposure Weighted LTVs for each portfolio segment.
Historic Recovery Rates are used to estimate Mean Portfolio Recovery Rates and Standard Deviation of Portfolio historic recovery pattern.
Lower Bound of LGD Distribution is fixed at 0% and Upper Bound is fixed at 100%.
Beta Distribution is used to estimate LGD for each segment with collateral and Implied LGD Generator is used for clean exposures.
Total exposure under each portfolio segment is divided into 3 buckets as per IFRS 9 guidelines.
All gross exposure within each segment that is less than 30 days past due (DPD) is summed under Bucket I together with any off balance sheet exposure.
All gross exposure within each segment that is more than 30 days past due but less than 89 days past due is summed under Bucket II together with any exposure where risk has materially changed since origination.
All net exposure within each segment that is more than 89 days past due is summed under Bucket III. Net exposure is estimated by reducing gross exposure to the extent of collaterization after applying prudent haircuts.
Provides a comprehensive end to end solution for the estimation of ECL provisions under current IFRS 9 requirements.
Supports all major IT platforms and browsers.
Easy to use & requires minimum data input from its users.
Equally capable of estimating ECL for large input data stream; as well as for a minimum of single quarter’s input data.
Comes with free on-site users training and very strong 4 tier technical support point forward which is available on 24/7 basis.
Provides free software updates as and when IFRS amends or refines its methodology.
Available in 3 versions to cater to varying needs of corporate clients.
Installation
Customization
Integration
Data Calibration
Test Runs
Stress Tests
Validation
User Training
Complete IFRS 9 estimations and disclosures in accordance with Simplified Modeling Method prescribed by Global Public Policy Committee (GPPC) of the representatives of the 6 largest accounting firm in their joint document issued in June 2016.
Comprehensive estimations of EAD, PIT PDs, and LGDs for counter party receivables including Banking and Sovereign receivables.
Strong technical support system with assistance from dedicated helpdesk.
Suitable for financial sector companies including banks, leasing companies, investment banks, mortgage finance houses, insurance companies, investment companies etc.
Customized to comply with reporting requirements under SPB circular 4 of 2019.
Consolidated solution including the software installation, country specific customization, full integration with bank’s internal database systems, and strong on-ground technical support with annual validation services.
Available in 3 versions to cater to varying needs of corporate clients.
In compliance with modeling methodologies agreed in GPPC document jointly published by 6 largest accounting firms.
Extensively tested by the Chartered Accountants worldwide.
Validated by globally renowned quants and verified by clients’ external auditors.
Excel versions of software already in use in over 56 companies across 17 countries.
Comes with free on-site users training and very strong 4 tier technical support point forward which is available on 24/7 basis.
Data assessment
Input & Output assessment
Operational feasibility
Data flow designing
Process mapping
Schematic designing
Operational feasibility
Data flow designing
System verification
Operational testing
Logistic sanctity checks
Estimation accuracy checks
National Client Coordinator
Regional Project Managers
Permanent Technical Members
Permanent Qualified Quants
Management Queries
Board Queries
Auditors Queries
Regulatory Queries
Data Verification
Parametric Validation
Output Interpretation
Disclosure Assistance
Trouble Shooting
Anomaly Management
Report Customization
Software Updates
