The financial services industry is constantly evolving, and data-driven strategies have become a crucial aspect of this transformation. With advancements in AI, machine learning, and analytics, financial institutions are leveraging the power of data to drive hyper-personalization at scale. This article explores how these technologies have revolutionized product development, marketing, and customer engagement in the financial services sector.
Om Deshmukh, Head of Data Science and Innovation at Envestnet Data & Analytics, believes that the real transformative power lies in the ability to unlock even deeper layers of hyper-personalization. Previously, financial services companies had access to vast amounts of data, but processing it at scale was a challenge. However, with the advancement of technology, it is now possible to turn this data into structured, tagged, and enriched information that fuels innovative product development and enables personalized targeting at an individual level.
Use Cases for Data-Driven Machine Learning
The insights delivered by data-driven machine learning are diverse and cater to various use cases. Financial institutions can leverage this technology to provide safe-to-spend notifications, analyze retirement goals, and offer individualized recommendations. It can also be used to target specific users who are ready for a home loan or open to a credit card upsell opportunity. Envestnet Data & Analytics has worked on several use cases, including detecting and analyzing inflation, identifying negatively impacted users, and providing timely warnings or additional support.
Unique Customer Targeting Opportunities
Data-driven strategies enable financial institutions to identify unique customer targeting opportunities. For example, a financial institution could proactively offer a loan repayment vacation to a loyal customer going through a rough patch. This once-in-a-lifetime opportunity showcases appreciation for the customer’s loyalty and strengthens the customer relationship. Another example is targeting customers who require post-pandemic assistance and tailoring promotional offers accordingly. By analyzing data, financial institutions can identify users interested in credit cards with airline points or users switching from ordering in to going out to eat.
Data-driven insights allow financial institutions to compare themselves with peer groups and local or national macroeconomic situations. This benchmarking is crucial in situations like a recent bank fallout, where quick determination of status and real-time course correction is necessary. The convergence of three factors has triggered this new level of innovation in the financial services sector. Firstly, the exponential growth of data creation has provided financial institutions with a wealth of data to work with. Secondly, access to pre-trained machine learning models has become democratized, making them accessible to organizations of all sizes. Lastly, the affordability and availability of computing power in the cloud have removed computational and cost barriers.
Experienced data and AI partners are invaluable in helping financial institutions navigate the complex landscape of data types and availability. They offer end-to-end machine learning systems, sophisticated engineering setups, and access to diverse and voluminous data required for generating personalized insights. These partners also enforce privacy and security measures to ensure customer comfort in acting upon the provided guidance. Machine learning models depend on data diversity to avoid biased inferences. Stratified sampling and leveraging diverse data sources ensure that insights and recommendations drawn are generalizable and reliable.
Data Enrichment and Customer Context
Data enrichment plays a vital role in eliminating the garbage-in, garbage-out problem. By adding customer context to every transaction, financial institutions can create detailed customer portraits and unlock new personalization opportunities. For instance, analyzing a customer’s various transactions, such as using a debit card at Starbucks, a credit card at a gas station, and a debit card at an ATM, provides valuable insights for targeted marketing campaigns, data-driven lending strategies, and more.
The power of data-driven strategies in the financial services industry cannot be underestimated. AI, machine learning, and analytics enable hyper-personalization at scale, transforming product development, marketing, and customer engagement. By leveraging these technologies, financial institutions can unlock the full potential of their data, enhance customer experiences, and drive growth in a highly competitive landscape. It is crucial for organizations to partner with experienced data and AI experts to navigate the complexities of data types, availability, and interpretability to harness the true power of data-driven strategies.
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