Commercial Lending Architecture and Operations

Leading Commercial Lending Architectures

Leading commercial lenders generally employ 3 core technology tiers across all lending products to take a deal from Origination through Servicing. Standardizing in this manner allows for platform consolidation, process consistency, and an enhanced level of exposure aggregation. The three tiers are:

  • Tier 1: CRM – used to manage RM sales pipeline, determine RM commissions, manage campaigns etc.
  • Tier 2: Credit Underwriting and Approval – a workflow based underwriting and credit approval system used to create an institutional history of the credit, move the deal through the approval pipeline, create a standardized credit package and capture electronic approvals
  • Tier 3: Loan Accounting and Servicing – the books and records subledger used to generate GL postings and perform day to day servicing activities

These technologies are knitted together with varying levels of integration to achieve operational efficiency and straight thru processing.

Advanced Techniques for Operational Efficiency

Once the three core technology tiers are in place, a variety of enhanced techniques to achieve operational efficiency become possible. These include:

  • Auto-Boarding – Creating an interface from Credit Underwriting to Servicing where key deal parameters are auto-boarded thereby eliminating the need to re-key and accelerating the time from Closing to Boarding
  • Integrated Risk Rating – Risk Rating is often completed using an offline model which requires data to be rekeyed and introduces potential data integrity issues. Leading institutions are able to share financial spreads between credit underwriting systems and risk rating models and then auto-input the calculated risk rating back into the credit workflow.
  • Exposure Aggregation – Implementation of a cross product credit approval solution is the first step in being able to aggregate exposure across product areas. However, there are credit events that occur post approval (drawdowns, payoffs, etc.) that need to be reflected back in the credit system in order to maintain an up to date and accurate picture of exposure. This is achieved by creating a daily exposure update interface from the servicing tier back to the credit underwriting tier.
  • Common Reference Data Sharing – Systems across the credit lifecycle rely on key pieces of reference data that will be ultimately used to account for and stratify a portfolio. Examples include: Customer Relationship Hierarchies, Facility Type, NAICS code, Geography etc. Rather than replicating these items across systems which is a maintenance and data integrity challenge, leading institutions create a single, centralized master reference data repository which is used by each platform to serve as a golden source of reference data for items such as pick lists and portfolio reporting.
  • Watch List – Watch List production is often a data mashing exercise and provides no way to show regulators correlation between counterparty risk rating and presence of a credit on the watch list. A leading practice is to eliminate a standalone watch-list and replace it with a watch-list that is completely generated from the risk rating engine based on a classification rule that specifies what level of rating is considered a watch list credit. Credits go on and off the watch-list via a rerating (upgrade or downgrade) process, ensuring that ratings and watch-list are always in synch.

Taking the First Steps – Target Operating Model

In order to achieve the level of integration and operational efficiency in the commercial lending lifecycle as outlined above, it is first necessary to understand the current state at your institution and develop a Target End State vision. This vision is then actualized via development of an implementation roadmap where smaller, achievable plateaus are delivered and a ‘big-bang’ implementation is avoided.

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