Financial Services Use Cases

Being able to utilise the massive volumes of data being generated in financial services provides the opportunity to automate routine back office tasks and rules based transactions and enhance customer experience.

Customer Insights

Operational Efficiencies

  • Price Optimization and Promotions
  • Channel Migration

Data Monetization


A Deeper Dive:

Predictive Models - Next Product to Buy Analysis

Predictive Models - Next Product to Buy Analysis
Commercial pressures mean the need to get ahead is vital for companies in the financial services industry. Retail banks, insurance companies, consumer finance companies and many others rely on the ability to anticipate which of their products and services would appeal to their customer base and identify the optimal time.

A retail bank might wish to understand which of its customers are most likely to be planning a major life event which could present the opportunity to sell a specific product such as a loan for a customer planning a wedding, or a mortgage / life insurance policy for a customer planning to purchase a home. Predictive models which analyse customers’ retail banking and transacting behaviour can provide strong indications that other customers are leading up to such an event in the near future.

The Challenge
Under GDPR specific and explicit consent is required to analyse customer behaviours for this purpose. The ability to build accurate predictive models is greatly reduced if there are significant consent holes in the customer data. Biased, overfit models will result in false positives, resulting in ineffective customer targeting and investment decision making

The Solution
Anonymizing all your customer data on the Trūata Anonymization Service means that predictive models can be developed, tested and evaluated against your full database of consented and unconsented users. This allows you to develop far more accurate and effective predictive capabilities compared to your consented user base alone, to provide optimal decision-making capabilities.

Longitudinal analytical capabilities are retained at an individual level in the anonymized data, ensuring that granular, individual behavioural data is tracked in the analysis. Your predictive models can be consistently refined, retrained and evaluated over time on our platform, ensuring that your business always has the most accurate and comprehensive view of customer behaviour.

Customer Profitability

Understanding and maximising customer profitability is a core activity of businesses looking to drive revenue and margin, particularly in environments where regulatory controls make differentiation challenging, increasing the time and overhead needed to bring new products and offers to market.

An insurance company may want to understand the profitability of individual customers across different products to develop complimentary product offerings and identify which customer segments would be most profitable to target for these products or services in order to maximise their revenues.
Customer profitability analysis
The Challenge
An effective enterprise wide customer profitability strategy requires extensive customer data analysis, segmentation and predictive modelling capabilities. Privacy regulations restrict a company’s ability to execute this effectively without the specific and informed consent from the user for this type of analysis.

The Solution
The Trūata Anonymization Service allows for a comprehensive suite of analysis to be carried out across your full customer data set. Customer segmentation analysis, profitability analysis and profitability forecasting can all be executed without the need for consent.

In addition, anonymized data can be held indefinitely, which allows clients to understand how previous investment decisions and customer engagement strategies have driven customer profitability over the long term.

Follow the links below to learn more about the use cases for the Trūata Anonymization Service.

Automotive     Retail      Telco    Travel and Hospitality