Meng is Flinks' Product Marketing Specialist. She articulates the value of Flinks' product that is pertinent to real-world businesses.

Unlock Financial Innovation with Predictive Data Modeling: 4 Real-World Use Cases

By Meng Guan on May 26th, 2022

In a world overwhelmed with consumer financial data, insights are the new gold for financial services to build valuable customer relationships that are beyond transactional.

Predictive analytics tools enable data-driven decisions by turning raw data into future insights, allowing businesses to leverage AI/ML-based models for a laser-focused understanding of a customer’s real-time financial situation and dynamics. 

Life event detection modeling is a sophisticated predictive method that allows businesses to forecast trends and customer behaviors. Flinks has expanded the analytics capabilities of Enrichment by including a set of core life event attributes to support various use cases, like loan default prevention, among others. 

By understanding each individual’s core life event data, financial services can provide more contextual engagements and create engaging experiences. They can embed actionable insights into a wide range of financial products throughout a customer’s lifecycle.

List of new attributes for life event detection

  • New employer and start date: new_employer_detected
  • Employer change and start date: employer_change_detected
  • Lost employer and end date: employment_loss_detected
  • New EI and start date: new_employment_insurance_detected
  • EI end and start date: employment_insurance_loss_detected
  • ‘Is retired’: retirement_detected
  • New ‘Entity’ and start date:
    • new_insurance_detected
    • new_telecom_detected
    • new_utility_detected
    • new_insolvency_loan_payment_detected
    • new_mortgage_loan_payment_detected
    • new_student_loan_payment_detected 

Flinks’ life event detection model will be the game-changer to help increase your ROIs and transform your customer experience. Here’s a sneak peek of 4 use cases demonstrating how some of our customers use Enrichment to unlock product innovation and create positive long-term results.  


1. Earned wage access (EWA)

Earned wage access (EWA) is on-demand pay, allowing workers to access a portion of their paycheck between traditional monthly or bi-monthly pay cycles. A survey found that among 200 mid-market treasury executives, 70% were already offering earned-wage access in some form.

Fintechs and digital banks can perform customer segmentation by incorporating life event data into their data fabric, predict the next pay, and deliver early wages through direct deposits or prepaid cards to employees, especially those who work hourly shifts or part-time jobs. 

Who can benefit from it and how?

Fintechs, Payment-as-a-Service, 
and digital banks

  • Unlock partnership opportunities through flexible EWA solutions with built-in risk management
  • Provide personalized % of earned wages at scale based on each individual’s projected payroll

Traditional banks and payroll processors

  • Streamline payroll operations for faster payments by reducing credit checks and admin costs
  • Facilitate new product offerings such as reloadable prepaid debit/credit cards for employees

2. Loan default prevention

Credit scores and transactional data are imperfect measures for lenders to gauge consumer creditworthiness, as that data does not contain information on possible negative life events, like an employment loss, that can reduce borrower cashflow and trigger a loan default.

Life event data can play a central role in risk controls to predict and prevent delinquencies: it is an accurate, point-in-time measure of the life event faced by individual borrowers at the time of default, which enables lenders to grant loans with confidence and better inclusion.

Who can benefit from it and how?

Agents and loan officers

  • Combine employment-related data with traditional credit data for a reinforced dual credit assessment
  • Deliver more flexible loan products by automating loan pricing, terms, and conditions

Underwriters and risk analysts

  • Predict a borrower’s likelihood to default based on adverse life events
  • Optimize cashflow underwriting models to approve more loans to subprime and thinner credit borrowers without elevating risks

3. Personalized finance

The world is going custom—72% of consumers rate personalization as “highly important” in today’s financial service landscape in a recent survey. And in financial services, data is key to personalization as it reveals unique circumstances of individuals’ traits, values, and underserved needs.

When it comes to hyper-targeted products, services, and recommendations based on customers’ banking behaviors, life event detection modeling can provide a human-centric approach to banks and credit unions for an advanced level of personalization.

Who can benefit from it and how?

Frontline employees of FIs

  • Increase customer lifetime value (CLV) by bringing contextual engagements into all customer lifecycle stages
  • Unlock revenue opportunities by identifying cross-sell/up-sell opportunities

Back office of FIs

  • Drive efficiency and cost savings with better processes and data intelligence
  • Build a strong recommendation engine scalable across different segments

4. Holistic financial planning

The holistic approach in wealth management is beyond an investment portfolio based on transaction data and financial modeling—it looks at every aspect of customers’ financial and personal life to figure out where they want to be and helps them get there.

Life event data has unprecedented value for wealth managers and robo-advisors to discover customers’ needs, patterns, and trends so that they can instantly capture the key “money-in-motion” moments for proactive and holistic advice.

Who can benefit from it and how?

Financial advisors & wealth managers

  • Establish trust by devoting more time to customer relationship building instead of sifting through documents 
  • Prioritize financial suggestions throughout customer lifecycle based on clients’ evolving needs

Direct-to-consumer robo-advisors

  • Provide richer advice at lower costs with AI/ML-based customer profiling
  • Promote financial inclusion by bringing high-quality financial advice to mid and lower net worth customers

Final Thoughts

Financial services must constantly look for new ways to make their experiences relevant and effective. To stay competitive, predictive data modeling is the springboard for financial innovations that you can’t afford to ignore. Talk to our experts to discover more use cases enabled by life event data and what our predictive insights can do for you.

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