Basel II has dropped the ‘one size fits all’ approach of the original 1988 accord, and allows
regulated institutions to adopt different approaches with varying levels of sophistication,
depending on the nature of the institution’s operations and aspirations.
Basel II seeks to trigger an evolutionary approach to both credit and operational risk, with
banks moving, over time, to more advanced approaches. The combined effect of introducing
capital charge for operational risk and making the credit risk charge more risk sensitive will
drive many institutions to refocus on their place in the overall value chain. This means not
only faster consolidation, but also faster polarisation of financial institutions along the value
chain. Three particular types of activity will serve as the focus for such polarisations:
• Originating and managing the value of customer relationships, i.e. the risk-adjusted
returns on customer relationships – the origination and risk-based ‘customer value
management’ strategies.
• Managing the credit risk-adjusted returns on the overall credit portfolio through active
credit portfolio management and back-end steering.
• Managing the operational risk-adjusted returns on transaction processing infrastructure.
At the heart of this evolutionary change is information technology. Technology is the key
enabler for effective data collection and collation, and analysis of risk and performance.
Adopting the right technology architecture is essential for moving an organisation beyond
mere compliance and ensuring true integration with key business and operational systems.
In this article, we aim to share our experience at RiskTech, having implemented many
different credit risk management systems for leading retail banks across the globe. We have
established through experience the best-practice for integrating business, operational,
finance and risk systems into a single integrated framework. In most cases, best-practice
credit risk IT centres around two key enterprise systems:
1. Customer Relationship Management
2. Credit Risk Management
Implementing one system without careful consideration of the other is likely to lead to
inconsistencies, data integration issues, inaccurate reporting and poor decision making.
The Customer Relationship Management System
Most effective retail banking customer management technology environments can be
defined using a customer-centric ‘five-box’ framework:
1. Data warehouse
2. Decision engines
3. Customer information database
4. Customer contact systems/delivery channels
5. Product systems
If the system used to capture or deliver customer information involves contact with
customers, it needs to include features which will support the management of the customer
relationship by prompting appropriate actions, based on customer needs and the value of
the customer to the bank. The method of delivering prompts will need to vary, depending
on whether the customer will see the output. For example, systems in a Tele-service Centre
can display full scripting and uncensored information, whereas systems designed for use
during face-to-face branch contacts need to be circumspect in the way that information is
provided, given the likelihood of the customer being able to view the data.
Customer relationship management systems should also be designed from the outset with
the facility to feedback the customer’s response to marketing contacts and to the Data
warehouse and Decision Engine.
For example, a customer may be calculated as having a high propensity to buy a personal
loan but has either:
• recently been offered a loan but refused it,
• recently applied for a loan but was declined by the bank, or
• just bought a loan.
Therefore, the customer interfacing areas should not receive a prompt to market a personal
loan at that point in time.
In essence, without capturing the customer’s responses to the bank’s approaches, the bank
is in danger of maintaining only a one-way dialogue with the customer. A two-way dialogue
should avoid informed contacts becoming inappropriate offers, and allow the Bank to learn
from its contact from the customer and apply such learning to maximise business benefit.
The key message is that all customer relationship management systems need to be
designed from the outset to deliver appropriate prompting, in both proactive, and reactive,
contacts with the customer, based upon increasingly prescriptive recommendations
produced from a central decision support system. The customer’s response can then be
captured and fed back into the decision making process and data warehouse.
Furthermore, new systems that involve customer contact need to be designed from the
customer perspective and not built in isolation to only serve the needs of individual delivery
channels or Product Marketing Units. Only by taking such a balanced approach can the
longer term needs of the bank be served, even though this may add to costs and timescales
in the short term.
Summary of the five-box framework:
1 Data Warehouse
Via a single logical data warehouse environment, to provide the ability to manage customer
value, predict & monitor customer behaviour, develop and apply customer segmentations
and manage customer contact, including credit and actuarial risk.
2 Customer Database
Via a single logical database, to provide the prime core customer data to be usable by
other component system within the Customer Relationship Management System.
3 Decision Engine
Via a single logical decision engine, to make, prioritise and manage proactive and reactive
decisions about/treatment strategies for customers. Information will come from, and
decisions will be implemented via other components within the Customer Relationship
Management Systems. Information on how the decisions were made and the outcome
(both branch observed and derived) will be provided at individual level, to allow analysis
and refinement, probably through the data warehouse.
4 Customer contact systems/delivery channels
Via a single logical contact system, the ability to deliver treatment strategies and other
customer decisions to customers and/or the customer facing staff, and capture/display
the contact history of customer responses.
5 Product systems
The product processing systems utilise information from, and provide information to, the
other components of the Customer Relationship Management System, including the
following:
• To not hold duplicate data
• To calculate or to feed the drivers of profitability to create product profitability and
hence allow the summation to customer profitability level.
• To be able to accept decisions generated by the credit information system.
