Synergies between Risk Modeling and Customer Analytics

Transcription

1 Synergies between Risk Modeling and Customer Analytics EY SAS Forum, Stockholm 18 September 2014 Lena Mörk and Ramona Klein

2 Agenda 1 2 Introduction Modeling in the financial sector Consequences from lack of alignment between risk modeling and customer analytics Achieving synergies in the organization Wrap up Page 2

3 Introduction Risk models Used in most financial services domains to ultimately predict profitability Customer analytics Marketing campaigns aim to increase customer base Customer analytics team models customer behavior in order to create target marketing Synergies Organizations allocate significant IT and human resources to preparing data and building models When the risk modeling and marketing analytics are performed in silos, organizations waste resources, time and do not achieve optimal benefits Effective data repositories and manipulation, appropriate modeling practices and model risk management can create synergies between risk modeling and customer analytics Page 3

4 Introduction Modeling in the financial sector Consequences from lack of alignment between risk modeling and customer analytics Achieving synergies in the organization Wrap up Page 4

5 Models are used regularly in the financial sector Pricing and Risk Analysis Predict loss cost Predict credit risk Build rate structure Build tiers Loss given default Features of predictive models: Various techniques, e.g.: Generalized Linear Models (GLMs) loss cost and conversion (insurance) GLM logistic regression (banking) Customer Analytics Predict demand Predict retention (e.g. loyalty scores) Risk segmentation Enhanced Decision Making Customer Value Others Predict fraud Predict reserves Evaluate sales force Statistical model diagnostics Validation techniques Ability to test predictiveness Ability to incorporate business judgment Not a black box (can follow model development, statistics, validation process) Page 5

6 Model development is similar across products and departments Preparation Data Single factor analysis Multi factor analysis Validation Implementation Project plan Establish scope Involve all stakeholders Gather data Prepare files for modeling Check and clean data Reconcile against other sources Initial data exploration Univariate analysis Correlation statistics Build predictive model Regression analysis (e.g., GLMs) Iterative process Statistical techniques to validate model structure Holdout samples to validate predictiveness Analyze competitive / profitability impact Incorporate constraints (e.g. business) Implement Documentation Quality assurance Experience and knowledge Monitoring Page 6

7 High quality data remains the #1 priority for building accurate models Predictive Model Setup Model structure Variable parameters e.g. loan size, insurance line of business, other products purchased e.g. elasticity of demand Validation Internal data External data In market behavior Product Interaction Marketing Company relations Billing Life stage e.g. postal code, proximity to coast proximity to fire dept. (insurance) customer behavior e,g. default or claim history, bill payment on time, existing loans CRM Other sys Geo-demographics Life events Lifestyle e.g. age, education, marital status Credit ratings Page 7

8 Working on the model inputs should be a collaborative effort across the organization Create a common data platform, while adhering to customer privacy and data protection guidelines External data In market behavior Interaction Real-time data Internal data Product More complete information Company relations Marketing Efficiencies Life stage Billing Maintain a common data dictionary across the organization Geo-demographics Life events CRM Reduce risk of errors Lifestyle Other sys Reduce data misuse Credit ratings Document the data sources and data manipulation Page 8

9 1 2 Introduction Modeling in the financial sector Consequences from lack of alignment between risk modeling and customer analytics Achieving synergies in the organization Wrap up Page 9

10 There are numerous sources of model risk and potential adverse business consequences Possible adverse consequences: Sources of model risk: Inputs Design Use/implementation in silos: Inadequate knowledge of model purpose, processes and controls, e.g. key person risk, lack of training Errors in the end-to-end process, e.g. unauthorized and incorrect model changes Overreliance on models, e.g. limitations being ignored Old-generation models unreliable as a result of changes in market conditions Financial (short-term, long-term) Accounting, Reputation, Poor decision making Ineffective marketing (limited up/crossselling) Customer loss Inadequate quantification of risk and capital requirements Incorrectly designed and priced products Poor strategic decisions Poor operations (planning, investment decisions and resourcing) Financial reporting errors and restatements...indicating the need for appropriate model risk management (MRM) and collaboration Page 10

11 Examples and consequences of model management in silos Examples Consequences Banking Marketing campaign attracts large number of customers who are disqualified due to bad credit Marketing attracts customers based on low risk of default without regard to their profitability Increased costs and bad will as credit departments spend time on rejections Resources tied up on customers with little profit margin potential Insurance Newly developed customer analytics department starts data aggregation process in new IT system Sales force and underwriters focus on high risk market segment to increase sales volume, but price them incorrectly New department would gain efficiency by starting with modeling data from risk department Adverse selection for the insurer: the increase in bad risks in the book of business leads to lower profitability Collaboration and alignment between modeling and customer analytics would have reduced the risk of model misuse Page 11

12 1 2 Introduction Modeling in the financial sector Consequences from lack of alignment between risk modeling and customer analytics Achieving synergies in the organization Wrap up Page 12

13 Three cornerstones of synergies between risk modeling and customer analytics your models Break down the silos Efficiency, profitability & customer value maximization your customers Page 13

14 Create synergies by effective risk management of business process and model risks Business purpose Break down the silos your models Efficiency, profitability & customer value maximization your customers Business process Financial processes Risk management processes Operational processes Inputs Internal data External data Assumptions Other model outputs Transform and cleanse inputs Development Implementation Model operation Validation Adjustments Outputs Outputs Other model inputs Business decisions Mgmt reporting External reporting Change management Risk management should focus on business process (e.g., resource pool, model results communication and implementation) and model life cycle (e.g., maintain model inventory, results documentation) Page 14

15 Create synergies by better understanding and using your models Break down the silos your models Efficiency, profitability & customer value maximization your customers Better decision making Bring together the objectives of the risk and customer analytics departments to optimize pricing efficiencies and marketing spend Improve management understanding of key models, assumptions and limitations Increase awareness of model usage and materiality Create an enterprise-wide understanding of what models are used, where they are used and for what purpose the range of model usage in the risk and customer analytics departments Consistent approach to managing models Adopt consistent development standards for new models and model changes across the organization Use resources efficiently for model review and validation Page 15

16 and leverage the data available across the organization External data In market behavior Interaction Company relations Real-time data Internal data Product Marketing Break down the silos your models Efficiency, profitability & customer value maximization your customers Life stage Billing Geo-demographics Life events 360 View CRM Lifestyle Other sys Credit ratings Customer Customer-Centric Analytics Segmentation Campaigning Customer Acquisition Customer Retention Cross-sell flags Up-sell flags Regression models Proactive models Agile analytics Accurate calculations Increased customer revenue Increased cross/up-selling Reduced time to market Benefits Improving risk management Improving customer satisfaction Improve customer retention Focus efforts on profitable customers One version of the truth Compliance with legislation Page 16

17 Customer information is scattered across systems and the organization - Break down the silos! Break down the silos your models Efficiency, profitability & customer value maximization your customers Align incentives with enterprise-wide value maximization rather than rewarding individual business units for volume generated Customer Agent channel Internet channel Mobile channel Other Channels Marketing Department Risk Modeling Mgmt. (Strategy) Siloed organization Finance and Operations Integrated business intelligence and analytics vision Customer-oriented organization A single view of the customer seeks to realize the financial benefits of the offerings by tailoring them to the customer needs Page 17

18 1 2 Introduction Modeling in the financial sector Consequences from lack of alignment between risk modeling and customer analytics Achieving synergies in the organization Wrap up Page 18

19 Wrap up Create a model inventory and ensure that it is used One of the most important tools when defining and recalibrating your strategy your models Align incentives towards customer-value maximization i.e analytics vision across product and line of business Break down the silos Profitability through customer value maximization your customers Secure a 360 view of your customer through shared data management Page 19

20 Thank you!

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