Exceed Your Client’s Expectations: AI Solutions That Every Asset Manager Needs
The Asset Management industry, like most industries, is in the midst of transformation. Longstanding pressure points and emerging changes are converging to create a unique, once-in-a-generation opportunity for the “buy side“ to harness the power of artificial intelligence (AI) and machine learning to exceed their client’s expectations while increasing client retention and increasing revenue.
At the same time, several major trends and shifts in the investment management business have converged in recent years, putting digital transformation and AI enablement at the top of every executive’s agenda. The widespread adoption of emerging technologies, such as AI and machine learning, combined with the increasing ubiquity of data (including customer and behavioral data) has presented a unique opportunity for asset managers to accelerate their use of predictive analytics in client servicing.
Here, we take a look at two ways AI can be used by Asset Managers to enhance the client experience while driving results to their own bottom line- client retention and using personalization for targeted marketing for cross-sell or upsell offers.
Predicting Client Churn
By deploying AI models, asset management firms can identify clients at risk of attrition by learning from examples of clients that have closed or moved accounts in the past. Preemptive client engagement can uncover issues driving dissatisfaction, reduce churn, and reveal opportunities for improvement in products and services.
Also, machine learning models can help firms understand the circumstances that are predictive of attrition, whether they be operational, service-oriented, or related to fee structure. Armed with a better understanding of what factors predict attrition, changes can be made to retain your best clients.
What results can a firm expect to see? Lower call volumes, happier customers, lower churn, and fewer challenging calls.
Pulling clients and advisors up the value chain of an asset management firm has long been a hallmark of successful investment management strategy. Understanding how your most loyal and profitable clients started out can inform how your firm should be targeting potential new clients and how to drive those clients up to your top tier.
At the same time, pushing indiscriminate offers to clients and prospects has been proven to be ineffective – and even damaging – in communicating with clients and prospects. With machine learning, firms can use their extensive client relationship data to identify which prospects, leads, and referrals are likely to become profitable and prioritize those. Asset managers can also predict which clients — or advisors — are likely to have a need for specific products and services. Firms can then make relevant offers at the right time, demonstrating client awareness, increasing the effectiveness of targeted marketing.
Lending Tree is able to achieve and provide personalized experiences to their customers by leveraging AI and machine learning.
“We’re empowering consumers in the most important financial decisions of their lives, and making sure that they have a choice, and can make a considered decision when going after this.” — Akshay Tandon, VP of Analytics, Lending Tree
Akshay and his team work to ensure that their customers understand the impacts of predictive models for their businesses and how the models work.
DataRobot has helped many large and small financial institutions build and deploy AI solutions like the ones described above. DataRobot’s automated machine learning platform allows organizations to increase their capacity and speed-to-market without having to scale or build expensive data science teams and analytics infrastructure. Download our eBook, 6 AI Solutions Every Asset Manager Needs, to learn more ways that DataRobot can help asset management firms accomplish all of these goals.