Tag Archives: LTV

Risk & Relationship based Pricing Part 5

This continues and completes the description of good risk and relationship based pricing principles.
Principle 6: Define cost allocation strategy and models
As a general principle all customers should pay the costs associated with the product they buy. The questions are:
1. What costs should be allocated?
2. With what level of granularity should these costs be allocated?
3. How are you estimating these costs?

In the ideal world the cost allocations should be done are the most granular level possible. This means, as an example, that the ATM costs should be allocated to each customers on the basis of the ATM usage by the customer and all ATM may have a different cost structure on the basis of location and other variables. This requires a very big and complex cost allocation system that can individualise all costs (and revenues) at client/ contract level. Teradata for example has such an application, but be warned it’s a big project to implement!
But is this good enough? Not if you just allocate past costs, as we are looking at future cash flows of revenues and costs! Those future costs will vary in time due to inflation, capacity, operational efficiency etc. This means you have to estimate future volatile costs based on historical allocations. Not impossible but definitely not easy.
What could be the consequences of allocating future costs as they are expected to occur, while revenues are dependent on the contract characteristics? Take a mortgage with fixed monthly instalments. The revenues of such a contract will be very high during the first years then very quickly drop to become small in the following years, while operating expenses for this contract will remain stable and even increase through inflation. The net profitability schedule will show higher profits followed by many years of losses! Actually this is the simple consequence of accrual accounting and should be adapted in management accounting.
Some banks want to allocate all costs down to the taxes associated with the product. That is actually easier to do, but is it correct? Should the customer pay the bank’s corporate income tax, or the Chairman and CEO’s airplane? Should the customer pay the cost of the Equity allocated to the products he buy’s.
In my opinion the answer is definitely no. The principle is that customers must pay the expected losses and the expected operational expenses (direct and indirect), but that the shareholders should pay unexpected risks and indirect costs, at least the indirect costs generated because of the nature of the corporate activity (being a bank) and thus including regulatory expenses, general management, audit and control costs… Yes these should be paid by the shareholders through reduced net profits (RoE) and dividends. An all-in cost invoiced to the customers would have them pay the inefficiencies of management and would make the banking products unaffordable.
Also core is the cost quantification model used. What cost accounting models should be applied? Average costing by product, by process or activity based costing…?
Decisions on the cost allocation strategy will fundamentally change product pricing and profitability. What is appropriate for your bank, your market, your global strategy? How is your bank analysing this/ Life Time Value of the contract, or if you prefer the Fair value of the contract after all costs allocations appears to me as the only financially correct way of giving a value to that contract/ client.

Principle 7: Define, quantify and manage current and future client profit and value contribution
It is evident that bank profitability and shareholder value requires appropriate pricing. But excess pricing can destruct value by killing growth and client satisfaction. To avoid that pricing must be “efficient” i.e. be fixed on the basis of risks and market/ client specific demand and price elasticity.
We believe this means that client pricing but consequently the client’s profitability analysis, must be granular and forward looking. Average profitability of products, multiplied by the contract size, which is added to the profitability also calculated on an average basis, will only give you an average historical profitability of little use to manage your client in the future.
Is your profitability analytics compatible with modern value metrics, such as the Customer Fair Value, which we can be summarised as the fair value of the client current product holding (multiplied by client behavioural variables) plus fair value of future product holdings (multiplied by the related client behavioural variables).

risk adjusted current and future client value

risk adjusted current and future client value

Such a model will highlight and individualise the behavioural sales/ marketing risks and allow the management of business uncertainties. LTV is metric to integrate and manage all client value drivers: profitability, growth, risks and time.
We will discuss customer value in détail in a future series on this blog.

Principle 8: Manage competitive pricing
How should the bank integrate pricing information from the market, from competition?
What should the bank do when it models a theoretical risk and relationship based price (for a product sold to a specific prospect) which differs from the market prices?

  • Reduce its price to meet competition?
  • Increase its price when it is lower than the market price?
  • Keep price unchanged?

A simple question but a very complex answer, requiring a multi-dimensional analysis!

  •  How to handle market inefficiencies and bank specific inefficiencies?
  • How do you integrate product life cycle and relationship life cycle?

These multidimensional analysis should allow you to understand pricing inefficiencies and market/ pricing opportunities, but don’t make the client pay for your inefficiencies and always manage pricing in a transparent and open way with the prospect client.
Remember abnormally high spreads can lead to immediate profits but negative values because of increased attrition. You need a long term value measure to manage your client over a long term horizon, and the at least the life time cycle of the product holdings!
Strategically you may want to sell a product at breakeven or at loss because you are buying market share or you have bundled that product with high profit/ value products. In all such cases you must calculate the loss and/or negative value generated for allocation as a cost to sales/ marketing.

Principle 9: Manage the complexity and risks of your quant models.
Bank management is complex because cash flows are volatile and behavioural. Managing that complexity requires deep understanding of the market, the client, and the product value drivers.
Each bank must adapt the complexities of its business models to its needs and markets. It should use quant models that are useful for its specific strategies and market constraints (often restricted due to weak data quality and availability).
From a practical perspective, the bank’s sales force does not need to understand all these complexities. You must package products in a way that can optimise the sales unit efficiencies.
Likewise clients do not need to understand all the complexities of the product. Managing risk and operational intermediation risks is what bank should do. They must package the products to make them attractive but without hiding any financial risks that you could be transferring to the product buyer. If you sell a variable rate mortgage inform the customer that there is risk in such a product for him (the bank has passed the financial risk to the customer because it will not or cannot manage the interest rate…). Finally be transparent in pricing, client will understand and appreciate this honest and professional attitude.
Ensure strong communication between your sales force and the clients, based on a simple list of core variables such as:

  • Price is dependent on risk and collateral,
  • Standard, off-the-shelf transactions are cheaper than tailor made products,
  • Pricing is a function of operational process, automated process (internet banking) will be cheaper than 1:1 branch operational support.

Principle 10: Adapt the organisation to the requirement of risk and relationship based pricing
A close integration of all risk management processes and policies with other parts of the process flows, will generate operational and management synergies. In other words banks should consolidate management analytics and resource allocations and break operational silos. This has often been said but not often realised! The evolution of data management and management analytics can achieve what enterprise culture has often shied away from!
Consolidation of management analytics and resource allocations will include Enterprise wide Risk Management (ERM) capabilities, with often new and specialised functional responsibilities (Risk Transfer Pricing, Economic Capital budgeting and allocation, Risk policy recommendations and strategy implementation…). On that subject please don’t look at ERM as an organisational issue (bring all risk functions under one umbrella headed by the CRO. ERM is much more that that!
Breaking operational silos is ensuring that the process flows are managed from client product request to product delivery in the most efficient and effective way, and is focused on client satisfaction and the bank’s performance criterias (KPIs) including operational capacity usage and cost efficiency, Economic Equity optimisation etc.
I recommend that such an integration project completes a thorough “impact analysis” on all aspects of the business model: Process, Organisation, Data, Applications and Technology, or PODAT analysis.

Principle 11: Integrate pricing process within an efficient process flow management system, from the origination of the client request to its fulfilment
Pricing is just one element in a chain of processes from origination to product delivery.
In the case of a loan these will include:

  1.  application analysis and qualification,
  2.  product matching to client requirements,
  3.  credit analysis – – scoring & rating,
  4. credit structuring & pricing,
  5. credit approval,
  6. documentation development and signature,
  7. verification of condition precedent,
  8. disbursement(s),
  9. contract management (interest payments, interest rate fixings…),
  10. collection.

Any delay or mismanagement of any of these activities (themselves usually divided in multiple operational, analytical and management process) will have negative impacts on the bank’s value drivers!
Control and automation of these activities and process are essential to sustain efficiency and strong business development.
The activities and process are data driven, rule driven and model driven. Without clear Policies and Procedures the bank cannot achieve operational efficiencies and respond to international standards of good management. The bank must manage the related operational risks and have strong audit trails on all activities.
Regulatory constraints and capital adequacy rules are dependant of these activities and must be fully integrated.

Principle 12: Integrate risk underwriting and pricing in an integrated sales and marketing strategy
Efficient process, good pricing models and strategies can result in very bad client management if behaviour analytics are weak and below standard.
We will define the behavioural analytical models as:

  • Credit scoring models that allow appropriate credit risk rating (for risk management purposes and/ or not for regulatory purposes);
  • Collection scoring for appropriate collections management;
  • Marketing scoring models that allow the development of appropriate client information (intelligence) such as sales propensities, attrition risks etc.
  • The client score card for credit risk and product ownership (business) risks is a core input in financial risk management (interest rate, liquidity) and capital management.

Risk appetite and risk allocation will be RAROC (or RORAC) dependant and global strategy must manage the Earning at Risk (EaR), due to all the risks underwritten by the bank.  An Adjusted Return on Risk Adjusted capital (ARORAC) is an interesting approach of integrating the bank’s solvency in the equation. We will define ARORAC as the RORAC minus the Risk Free Rate (RFR), divided by the banks’ market beta.

Principle 13: Optimise risk appetite, risk budgeting and allocation and pricing towards clear and precise performance criterias
The 2008 – 2012 great banking crisis has imposed stricter internal risk management constraints not only in capital adequacy but also and possibly more importantly in the bank risk policies, strategies, models and process of risk management.
Consequently this puts new challenges in the Financial Control function of the bank and in its management accounting policies and procedures.
In parallel financial accounting (IFRS) is implemented and includes different approaches to the risk management concepts defined under the Basel Accord and Best Risk Management Practices(BIS).
All the approaches are valid, but different hence they need to be reconciled!
Shareholder Capital is at a premium. It is rare, expensive and insufficient to cover all the needs of the market. Strict Economic and Regulatory Equity management is an absolute requirement. This translates into new solvency management requirements.
Banks develop new Key Performance Indicators (KPIs) to meet these challenges. They must be integrated into pricing to generate value manage value drivers.
This can only be achieved if pricing is relationship based and risk based.

This concludes the series on risk and relationship based pricing. I realise that this helicopter view of the “best of class” approach can seem excessively complex for many banks and markets. High competition, highly volatile markets, the increasing complexity of products sold and the increasing weight of regulatory constraints, will force all banks to enhance pricing models and strategies.
I hope my view of the subject will generate comments and discussions.
Please don’t hesitate to add yours on the blog.
Next series will cover customer value metrics.
I will then start a new series regarding a new approach in credit risk management using available but rarely used data. I’m talking of Event Based Credit Management, which I am developing with a good friend and partner, Mark Holtom. Mark is founder and general manager of eventricity an expert in Event Driven marketing. I strongly recommend you check him out on www.eventricity.biz


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