1. The Next Step in the Analytics Arms Race
Connected Decisions Across the Lifecycle:
The Next Step in the Analytics Arms Race
TRMA Spring Conference 2017
Jaime Vargas
Director, Credit Risk Strategy, T-Mobile Financial Services
Tim VanTassel
VP General Manager, FICO Solutions and Consulting Group

2. Past Video https://fico.box.com/s/xjd571jkjrggfz2muhsabznn7qj21cpo
Video: (0:48 – 2:00) is roughly the section of the video we want to use. Within that section, we need to try and scrub out any unnecessary mentions of FICO.

3. Trends Driving Connecting Decisions – Followed by five use cases
Past
Present
Regulatory scrutiny
Emerging focus on fair and equal treatment
Purchases made in person. Decision time moving from weeks to days
Buying experience
Intensive focus on fair and equal treatment and disparate impact
Need for transparency/traceability
Consumer data availability
General Purpose Credit Score
One-to-Many Marketing. Purchases made in person and digitally. Decision time must be seconds
More Data. More Scores
Credit Risk using non-traditional and internal data
Customer Retention/First-party Fraud

4. Story #1: Marketing outreach connected with credit pre-screening
How is this different?
Anyone can employ the risk screening techniques used by financial services in concert with current marketing campaigns.
Many carriers that are working to expand into new geographies and segments are realizing that adding regulated data is useful.
Carriers are implementing prospect databases that allow regulated data to be paired with marketing data.
Pre-screened targeted outreach goes into the segment with a higher end to end pull through.
Customers are happier as applications are met with an “Approval”.
Additional analytics can be brought to bear.

5. In store/online channel (no direct mkting).
Story #2: Browsing customer data connected with pre-screened custom offers
How is this different?
Prescreen-of-one functionality allows customer to be instantly screened for credit in response to real- time interaction.
In store/online channel (no direct mkting).
Bill is looking at the website thinking about new phones (at 1:30am). He has not applied.
Bill is steered to a customized offer based on his device/browser that has been pre-screened for credit without a full application.
Bill accepts offer and completes application. Application is automatically approved. Bill never went looking for what was best for him. ✓
Bill is a new customer!

6. Story #3: Loss predictions connected with better plan choices upfront.
How is this different?
Customer credit segments have device- level loss forecasting predictions that enable more precise plan/device guidance
Optimization analytics guides to better plan offers balancing uptake, risk, and profitability
Jane walks into store looking for phone and wants an iPhone
Jane applies for plan
Loss forecasting highlights that this is higher risk device requiring a sizeable down payment.
Jane is presented an offer without a down payment for a lesser phone including a broader friends and family agreement
$ vs. $
Jane chooses the lesser phone despite wanting the iPhone and leaves the store with service

7. Story #4: Fraud attacks are increasing, faster and more complex than before..
How is this different?
Fraudsters are attacking faster, requiring Carriers to deploy real time intra day enterprise wide capabilities to protect all channels & products
Subscription, Application, ID, First Party Fraud is the largest loss fraud type and is increasing globally
High Cost Smart phones are still being targeted and extend into abuse of Price Bundling, Subsidy & Handset Financing
Fraudsters manipulate both real and synthetic identities
Carriers can benefit from continuous fraud prescreening to improve customer experience and weed out fraud earlier

8. Story #5: Customer information connected to better collections outcomes
Too often, customers are all on the same track for collections. This is changing.
How is this different?
Rigid collections processes are starting to be replaced by a more flexible informed strategy.
In some cases, pre-delinquency treatment notifications are being run. !
Moving to a much more segmented treatment of the customer.
Most importantly, the focus on “past due” is insufficient relative to customer exposure.

9. Analytic Insights Driving Credit Risk Management Innovations
Past
Future
Present
Distrust of machine learning & AI. Need for guardrails and explanatory AI
Regulatory scrutiny
One-to-One Marketing. Purchases made digitally. Expects decision before they ask
Buying experience
Consumer data availability
Big Data sources abound. Huge data sets can be stored and accessed cheaply.
Content and services

10. Future Video

11. Questions?

12. Thank You