10.1 Overview of the Process

Oracle Financial Services Balance Sheet Planning (OFS BSP) is designed to model balance sheets under a variety of rate environments. OFS BSP functionality uses several key concepts and has evolved from the continual iterations of building simulated management processes. The basis of OFS BSP functionality includes:

  • The ability to model account-level detail to precisely capture the complex product characteristics within a financial institution's portfolios.
  • A flexible time horizon is critical to meeting the wide range of forecasting requirements of financial institutions.
  • Unconstrained chart of accounts definition is a basic requirement of effective modeling.
  • A structured process for defining and controlling assumptions is critical to any successful modeling process.

Key Operational Concepts

OFS BSP includes six key modeling attributes:

  • Modeling Using Account-Level Data
  • Separation of Data from Assumptions
  • Separation of Assumptions Types
  • A Structured Approach to Defining Rate Scenarios
  • Flexible Chart of Accounts
  • Powerful Assumption IDs

Modeling Using Account-Level Data

OFS BSP models data at an account level (loan-by-loan and deposit-by deposit). In this way, cash flows are precisely modeled based on the unique characteristics of each loan and deposit.

Separation of Different Types of Assumptions

By separating data from modeling assumptions, you can modify assumptions and then run a reprocessing procedure without affecting data. In addition to the instrument data being separate from assumptions, each type of assumption is defined independently from other assumption types. Although they act dynamically during a processing run, assumptions about new business characteristics, new business volume, new business pricing, new business maturity mixes, and prepayments are defined independently. This facilitates the isolation of the impact of a change on one assumption.

Structured Approach to Defining Rate Scenarios

Structured testing of alternative rate environments is a key to a strong “what-if” analysis process. The model provides numerous utilities for defining and quality controlling rate scenarios. Because rate scenario modeling assumptions can be saved as patterns in Forecast Rate Scenario rules, the typical burden of quality controlling rate scenarios is dramatically reduced. Once a pattern of rate shocks, spread changes, or yield curve rotations (or combination of the above) is defined, it can be saved as a Forecast Rate assumption rule to be used again and again. When data are updated in the model, all you need to do is update the base position of key market rates. The change pattern in an assumption rule can be applied and the forecast rate assumptions are automatically updated. It can also easily be cloned and modified to be saved as another Forecast Rate assumption rule. This approach for defining rate scenarios is ideally suited for testing numerous rate environments in a controlled fashion.

Flexible Chart of Accounts

You can define an unlimited chart of accounts (Dimension Member Values), incorporating all of the key elements which drive cash flow generation.

The Power of Assumption Rules

You can mix and match any set of combinations of assumptions: forecast rate scenarios, pricing margins, maturity mix strategies, prepayments, product characteristics, and so on. The separation of each element of the scenario assumptions means that you can incrementally modify one piece of the modeling equation and easily test its effect.

How the Model Works?

While the specific operation of each section of the model is addressed separately in this guide, a general description of the modeling logic includes the following premises:

  • The current position data defines the existing base of transactions.
  • New business volumes are generated within OFS BSP's planning user interfaces.
  • The maturity mix of new volumes is defined by assumptions.
  • Pricing of new volumes and repricing of existing volumes are defined by rate scenario assumptions and the contractual pricing characteristics of individual transactions.
  • Dynamic prepayment assumptions can be applied to any account.
  • Cash flows are determined through the integration of data and assumptions.
  • Assumptions can be flexibly combined.

Current Position Data

OFS BSP forecasts are based on modeling the behavior of existing transactions, as well as those that originate in future periods. The complete cash flow characteristics of each existing transaction are defined in the data structure that is imported into the OFSAA data model on a loan-by-loan and deposit-by-deposit basis.

Note:

A System Administrator is responsible for producing a current position forecast that is subsequently used by Planning Administrators and Planning Users within BSP's planning user interfaces.

New Business Volumes

New Add volumes in each period are defined on the basis of a forecast of new incremental volume for each account or calculated on the basis of achieving a “target balance” in the account. The cash flow characteristics of newly originated volumes are determined by the Product Characteristics definitions. New volume assumptions apply to non-interest income and expense accounts as well.

Note:

All New Business volumes are generated by planning users within BSP's planning user interfaces.

The Maturity Mix of New Volumes

The maturity mix of forecasted volumes originated for an account is determined by assumptions that are applied to each element of the account.

Note:

In BSP, Maturity Mix data is applied to New Business volumes generated within BSP’s planning user interfaces.

Pricing of New Volumes and Repricing

Pricing of newly originated volumes, or repricing of adjustable and variable rate volumes, is determined by the integration of several factors. Each existing or newly originated transaction is linked to a single rate (a market rate) or a yield curve. The actual rate determined at origination or repricing takes into account the prevailing single rate or yield curve. The term of the transaction (if pricing is tied to a yield curve) is also taken into account, as well as other pricing characteristics (margin, life cap, period cap, and so on.). Incremental pricing margins can be applied to new originations on a period-by-period basis for each account.

Note:

All New Business pricing is generated by planning users within BSP's planning user interfaces.

Dynamic Prepayment Assumptions

You can apply dynamic prepayment assumptions to any account. Prepayments are applied on a loan-by-loan basis. Prepayment assumptions use individual instruments characteristics to drive prepayment behavior.

Cash Flows

Cash flows for every instrument are calculated in every modeling period according to the contractual terms defined in the incoming data and/or product characteristics definitions, combined with interest rate scenario and prepayment assumptions. Principal and interest cash flows are recalculated as contractually defined.

Batching Assumptions

Financial forecasts are built through the integration of current position data with each of the modeling scenario assumption elements:

  • Current Position Data (account-level data)
  • Forecast Rate Scenario Assumptions
  • Forecast Balance Assumptions (collected in Planning interfaces)
  • Maturity Mix Strategies
  • Pricing Margin Assumptions (collected in Planning interfaces)
  • Prepayment Assumptions
  • Product Characteristics Assumptions

Flow of the Modeling Process

The organization of the OFS BSP menu structure is better understood in the context of the general flow of the modeling process, as follows:

  1. Set up Application Preferences, including:
    • Certain elements of Application Preferences (as of date for data, modeling leaves, and so on)
    • Default values and limit details
  2. Load transaction-level data.
    • Load data into the system for modeling.
  3. Perform cash flow edits.

    Each of the individual instrument records must be quality controlled. For each instrument record, the cash flow edits check all of the columns used in cash flow calculations for internal consistency. For example, the maturity date must be greater than the origination date, and the payment frequency must be greater than zero. This process is available within BSP Processing and is a critical step in ensuring that OFS BSP produces expected results.

  4. Define modeling assumptions.

    Define assumption sets. These include any number of the assumptions described in this module and will minimally include Time Buckets, Product Characteristics, and Forecast Rate Scenarios.

  5. Specify the model run. Select a combination of assumption sets that will be applied to the data you wish to model. You may define a single processing run, or a batch of several runs, including multiple sets of assumptions. The run or batch is launched and results are produced into results tables.