Risk Rating

To qualify for the supervisory approval of an IRB approach a bank must:

• Quantify the various credit risk components (that is, PD, LGD and EAD) which, when taken together, constitute the elevated level of credit risk management required under the IRB approaches
• There must be enough meaningful statistical observations so that the quality of risk assessments can lead to realistic estimates
• In order for an internal ratings system to be IRB compliant (that is, it is eligible to be used for regulatory capital purposes and, more generally, to deliver sound risk assessments), banks must collect substantial amounts of historical data.
• Must gather data systematically

Historical data refers to all data related to events that have affected exposures over a given period in the past.

BASEL II Standardized

• Credit Exposures (on and off – balance sheet) information
• Collateral Information
• Long and Short Term Credit Ratings by External Credit Assessment Institutions (ECAIs) recognized by the Central Bank
• Mapping of ratings of ECAIs with Central Bank Rating Grades

BASEL II – IRB

• Credit Exposures (on and off – balance sheet) information
• Collateral Information
• PD estimates should be developed using a minimum historical observation period. This includes historical default data.

Supported Credit Analytics

Combining the best of tested and established rating practices and the power of a Business Intelligence platform, Vision Insights generates an useful set of reports and analytics for use by the credit risk management function.

Customer Scoring and Dashboard

A fully configurable scoring engine can be used to replicate scores based on existing criteria, or to define and test new attributes. Scores are computed for multiple periods and rating migrations from one rating grade to another are incorporated in the probability of default calculations.

Key Features

• Multiple multifactor configurations
• Attribute selection and user defined sensitivity
• Frequency distribution and mapping
• Score composition
• Percentile standing
• Customer score tracking
• Dataset management

Customer Credit Profiling

Build detailed customer profiles using customer account information, geographical location and business specific attributes including asset class, legal status, incorporation date and industry.

Annual, semi-annual and quarterly financial statements are imported and categorized. In order to handle data availability issues, the template used for capturing and importing data is highly customizable, providing flexibility of using summary level data, or an entire balance sheet breakup.

In addition to borrower specific characteristics, a second dimension also looks at facility specific characteristics including product type and structure, limits, collateral and utilization, from which facility risk rating and estimates of loss given default may be derived.

Key Features

• Balance sheet import and classification
• Ability to define and modify new scoring elements
• Attribute type and subtype definition
• Import functionality
• Rank and rate profiles based on relative criteria and credit score
• Prune and trim portfolios based on relative ranking and rating
• Funded vs. non funded facility classification
• Facility specific definition

Provisioning for Delinquencies

The provisioning module allows analysis of delinquencies at a customer or facility level, with the ability to drill down and look at specific sectors, industries or by any other attribute.

Key Features

• Regulatory or Internal definitions for

• • Classification days past due
  • Provision requirement
  • Forced sale value

• Subjective downgrades

• Restructuring

• Stress test

• • 9Increase in days past due
  • Continuous deterioration in nonperforming loans

• Tracking of

• • Provision shortfall
  • Provision recovery
  • Provision held
  • Principal repaid
  • Projected profitability by customer, sector, segment, days past due and product