Approach, Solutions, & Benefits Case Studies

Use Case
Financial Service organization had missing, duplicative, and inconsistent Customer and Counterparty data.

Existing Process
Large scale implementation of Master Data Management (MDM) systems with manual rules matching and 6+ months to implement to achieve at best 85% quality.

Our Approach
ZettaSense wizards were used for Individual or Institutional party entity resolution wizard.  Business and Data SMEs trained system to resolve and clean data with little to no coding involved.

Benefit
Able to achieve 96-98% accuracy rates through automation; delivered clean Customer data within 2 months and reduced Total Cost of Operation (TCO) of up to 90%, with savings greater than $3M annually.

Use Case
Financial Services Client unable to meet customer data privacy regulatory requirements, especially “Right to Forget.”

Existing Process
Manually driven meta-data tagging of Customer personally identifiable information and searching/deletion of customer records in business systems individually to meet “Right to Forget” requirements.

Our Approach
Used ZettaSense to find and master customer data across hundreds of structured data sources (including databases, files, and spreadsheets) and feed Data Privacy Tools directly to handle record deletion requests.

Benefit
More accurate execution of customer data deletion requests — over 10x faster (from avg 64 min per request to 5 min per request).

Use Case
The bank’s VAR pricing model was taking too long to run. 

Existing Process
The complex VAR model ran on an expensive grid, would take 50+ hours to run and could only run once per week.

Our Approach
Run the VAR model on the ZettaInsights platform, reducing model run time to just 45 minutes, and re-pricing 3 times per day. 

Benefit
ZettaInsights improved decision-making on capital allocation. A 4-5 bps improvement led to $37 million in risk capital being freed up for investment. The client saw an 80% reduction in total operational cost, resulting in $5 million in annual savings.

Use Case
The client was experiencing lower bill payment receivables due to sub-optimal Collections Operations.

Existing Process
No advanced analytics capabilities were in place.  Allocation of Collections resources on missed payments was handled on a first-come, first-served basis.

Our Approach
ZettaInsights implemented its out-of-the-box Bill Defaulter prediction model.  Collection resources were allocated based on the highest likely customer value. 

Benefit
The overall improvements to Collections resource allocation led to a 1-2% improvement in Collections AR, amounting to $3.2 million per year.