Presented by Envestnet Data & Analytics
In today’s financial services world, the use of AI, machine learning, analytics, and more is transforming the industry. These data-driven strategies are driving hyper-personalization at scale, which is crucial for product development and marketing success.
“What’s truly transformative is how these technologies have unlocked even deeper layers of hyper-personalization,” says Om Deshmukh, head of data science and innovation at Envestnet Data & Analytics. “Financial services companies now have the ability to process vast amounts of data, turning it into structured, enriched data that accelerates product development and enables targeting at an individual level.”
The Power of Data-Driven Machine Learning
Data-driven machine learning has the potential to deliver valuable insights and drive innovation across various use cases in the financial services industry. Some examples include:
- Safe-to-spend notifications
- Analysis of retirement goals
- Individualized recommendations
- Targeted marketing for home loans and credit card upsell opportunities
Envestnet Data & Analytics has been actively working with clients to implement these use cases. They have helped detect and analyze inflation, identify users at risk, and determine appropriate actions. They have also assisted clients in targeting customers in need of assistance after the pandemic and creating personalized promotional offers.
“Think of it as a loan repayment vacation for three months where the FI can proactively tell them, ‘you’ve been a loyal customer with us for 10 years, we think you may be going through a rough patch — would you like to stagger your monthly repayment?'” Deshmukh says. “You’re offering a once-in-a-lifetime opportunity to show your customer how much you appreciate their loyalty.”
Furthermore, data-driven insights can help financial institutions benchmark themselves against peers and local or national economic situations. This is vital for quickly assessing their status and making necessary adjustments. The convergence of three factors has led to this level of innovation:
- An abundance of data being generated every day
- Accessibility to pre-trained machine learning models
- Affordable and accessible computing power through cloud technology
Experienced data and AI partners play a crucial role in guiding companies through the complex world of data types and availability. They offer ML systems, engineering setups, diverse data sources, and ensure privacy and security.
“We’ve been leveraging machine learning to make data-driven products that touch millions of end users’ lives,” Deshmukh says. “We’ve also been building on the diversity of the data that we have, with very systematic, automated checks and balances to uncover bias or irrelevance, or detect anomalies.”
The Importance of Data Diversity and Enrichment
Data diversity is essential for training accurate and unbiased machine learning models. Stratified sampling, where data is sampled across various dimensions specific to each financial institution, can enhance generalization capabilities.
Additionally, data enrichment plays a critical role in eliminating the “garbage-in, garbage-out” problem and adding valuable customer context to transactions. Each financial transaction provides important information that can contribute to a detailed customer portrait, enabling precise targeting and data-driven lending strategies.
To learn more about the valuable use cases, navigating the world of data, and more, don’t miss the VB Spotlight event presented by Envestnet Data & Analytics. Register now to watch for free!
Agenda:
- Introduction to AI and Analytics in Financial Services
- Use Cases and Real-World Examples
- Navigating Data Requirements
- Expert Panel Discussion
Presenters:
- Om Deshmukh – Head of Data Science and Innovation, Envestnet Data & Analytics
- Other industry experts and thought leaders
