Applying AI in Asset Management Distribution

The Data Dilemma in Driving Growth

Charlie Shaffer, President & CRO, TIFIN AMP

September 2024

About the Author

Charlie Shaffer President & CRO, TIFIN AMP

Prior to joining TIFIN AMP, Charlie had a 30+ year career in global distribution across investment management, advisory and implementation solutions. Charlie began his career at Morgan Stanley in 1993 where he helped build and lead Morgan Stanley's pioneering effort in providing risk management and implementation solutions to asset managers and owners. He then went on lead Global Distribution for Credit Suisse Asset Management, Voya Investment Management and Russell Investments.

Applying AI in Asset Management Distribution

Modern Asset Management firms are navigating a rapidly evolving, data-rich landscape, where the sheer volume of information can be overwhelming. Despite having access to vast amounts of data, deriving actionable insights and seamlessly integrating them into distribution workflows remains a significant challenge. The reliance on outdated CRM systems, once effective, has resulted in mounting "tech debt." These legacy systems struggle to manage the complexities of today's data environment, hindering growth, stifling innovation, and impacting overall performance.

To thrive in today's competitive distribution environment, asset managers must transcend their outdated systems and platforms. Historically, client data management evolved from early ledgers and rolodexes to databases and cloud-based SaaS CRM systems, all following a similar model of organizing backward-looking contact data into fields, tables, and dashboards. While an important piece of the puzzle, asset managers need to move beyond these legacy frameworks. The new paradigm of client data requires a fundamental shift in how data is managed, analyzed, leveraged and integrated. Leveraging supervised artificial intelligence (AI) offers a transformative path forward, promising to revolutionize the way asset managers distribute.

The core challenge for an Asset Management Distribution platform today lies in the fragmented nature of asset management data and legacy platforms' inability to manage it. Information is scattered across various silos. The proliferation of systems, internal teams, vendors, and external sources, creates barriers to achieving a cohesive, unified view of clients and potential clients. This data fragmentation undermines decision-making, reduces operational efficiency, and elevates the risk of errors or missteps.

Applying AI in Asset Management Distribution

To highlight the complexity of the data problem, we've mapped the five primary data categories that an advisor-sold or 'intermediary' Asset Management Distribution team has to manage:

• Trade & Asset Resolution (e.g., Broadridge SalesVision, SS&C SalesConnect, SalesPage, MARS, Internal Build)
• CRM & Engagement Data, Internal Data Arch. (e.g., AWS, Salesforce, Snowflake, Marketo, Microsoft Dynamics 365, eloqua, HubSpot, Microsoft Azure)
• Service Provider Competitive Data (e.g., Broadridge Opportunity Hunter / Global Market Intelligence (GMI), Envestnet, SS&C Walletshare, AssetMark)
• Broker-Dealer / Wirehouse Data Packs (e.g., Morgan Stanley, LPL Financial, Wells Fargo, UBS, Merrill Lynch, Raymond James, Fidelity, Edward Jones)
• Third-party data aggregators (e.g., Dakota, Discovery Data, FINTRX, AdvizorPro, RIA Database)

The rapid growth of data has outstripped the capabilities of traditional CRM platforms to manage and utilize it effectively. Consequently, legacy systems are falling short, leaving Distribution teams and Sales reps to juggle multiple dashboards, spreadsheets, and CRM contact fields just to prepare for a single meeting. As data volume and velocity continue to surge, the Tech Debt compounds. Conventional data management approaches struggle to keep up. Despite valiant efforts from IT and BI teams to address these issues, the outdated platforms they rely on were not built to handle such challenges, leading to fragmented data and a lack of actionable insights. To address this problem, it's crucial to understand the root causes and how we arrived at this point.

Applying AI in Asset Management Distribution

An Industry Built to Look Backwards

Salesforce recently celebrated its 25th birthday, boasting nearly 90% market share in Asset Management. Its market dominance is a testament to its success, but it is constrained by the fundamental limitations inherent in CRM technology since its inception: it is designed to be backward-looking. Like early ledgers and manual rolodexes, CRMs were originally built to manage and organize customer relationship data with a focus on historical information. Their primary purpose was to answer questions like "Who are my clients?" and "What products have I sold them?" Although CRMs have evolved to support sales campaigns and initiatives, their core structure remains rooted in tracking and organizing past interactions, not in navigating the complexities of future growth. Consequently, they fall short in managing the intricate data landscape that asset managers need to drive growth.

Asset Management Distribution teams face a significant challenge in adapting their platforms to be forward-looking. After all, their core features are centered around contact management, sales pipeline tracking, and basic reporting. And as the universe of Asset Management products have evolved from early partnerships, to pooled funds and now into Mutual Funds, ETFs, SMAs and Private Markets, and as the advisor-sold market becomes increasingly complex (Hybrids, Breakaways, Teams, etc.) - the challenge of simply organizing the backwards looking client and product data has grown in complexity. Integrating CRM data across middle and back office systems to track and manage customer data has been an enormous challenge. Trying to simultaneously turn these platforms into forward looking, prospecting and client origination platforms has proven to be an extremely costly and elusive goal.

As Asset Management Distribution teams began to realize the fundamental challenge of converting the backwards looking CRM platforms into forward looking distribution growth engines, many firms chose to build costly data architectures around their CRMs to try and force them to look forward. The traditional playbook for this was to engineer massive retrofit of their CRMs and build complex data architectures around their CRM systems.

Applying AI in Asset Management Distribution

The playbook for this process often looked like this:

• Engage technology consulting firms
• Establish framework for Data Lake, Data Warehouse & Processing (Azure/AWS, TIBCO, Snowflake, etc.)
• Design and deploy middleware solutions to connect into Data Warehouse
• Integrate CRM and Trade and Asset Resolution data
• Build out Analytics and Reporting platform (Alteryx, Tableau, PowerBI)
• Integrate into Mar-Tech stack (Adobe, Marketo, Salesforce, Seismic, Concur, Outlook, Seismic, etc.)
• Integrate third party Data Sources

This approach, while ultimately driving in the right direction, was enormously costly and slow. Furthermore it often compounded the "Tech Debt" as it introduced in-house data architectures that required extensive maintenance and support. But the primary driver of the tech debt was that the expanded platforms still failed to solve the core problem of data unification, predictive insight generation and integration of insight delivery into the "last mile".

While CRM systems have been an invaluable tool for asset managers, their limitations are becoming increasingly evident. And in trying to force them to look forward, Asset Managers have collectively spent billions of dollars doubling down on CRM as the center of their data solar system. The results have been mixed at best - and have left many Asset Managers realizing that to overcome these challenges and unlock the full potential of their data, they need a different approach.

Applying AI in Asset Management Distribution

The Solution: Supervised AI to Make the Data Look Forward

The limitations of traditional CRM systems and the expanded Asset Management Distribution data architectures that have emerged around them has created a pressing need for innovation. Artificial Intelligence (AI) emerges as a powerful tool to address the challenges faced by asset management distribution platforms. But without the expertise in unifying the data required to power insights, the promise of AI won't be realized.

By unifying data and marrying it with specific, supervised AI capabilities, asset managers can overcome data fragmentation and unlock valuable insights hidden within their data to make it look forward and drive growth. Algorithms can analyze vast datasets, identify patterns, and generate granular, actionable recommendations, enabling far more efficient engagement and conversion. Integrating these insights into the Distribution workflow to solve the "last mile" problem unlocks its power. By embedding feedback loops as a core part of the user experience two further benefits can be derived. First, this drives adoption – salespeople finally have a mechanism to interact with intelligence and see it improve, and second, human-in-the-loop feedback is like rocket-fuel to modern algorithms. The result: enabling high-cost distribution professionals to cover more of the market with greater efficiency.

TIFIN AMP's operating model as an Asset Manager's data engine

Applying AI in Asset Management Distribution

Selecting the right partner to deploy supervised AI capabilities can revolutionize an Asset Managers' distribution function by turning the organization forward and providing the following benefits:

• Industry-specific data unification and harmonization: Vertically-focused, supervised AI can seamlessly integrate data from disparate sources, creating a unified view of clients, prospects, channels and markets, specific to the your business.
• Automation of routine tasks: Algorithm-driven automation can streamline processes, reduce operational costs, and free up human resources for more strategic activities.
• Enhanced data quality: AI-powered data cleansing and validation tools can improve data accuracy and consistency, ensuring reliable insights.
• Predictive analytics: By marrying data unification with bespoke models that analyze historical data and identify patterns, supervised AI can build insights and continue to learn and evolve as the engagement and usage accelerates – with automation and scale.
• Personalized client experiences: Applying AI can enable "personalization at scale" by analyzing individual preferences and behaviors.
• Solving the "Last Mile" with Integration at the point of sale: Supervised AI can deliver synthesized, actionable insights seamlessly within a user's experience, that are easily and readily consumable by the wholesaler with minimal incremental effort.
• Dynamic Prioritization and Feedback loops: Supervised AI algorithms put the power in the hands of the sales team by allowing then to engage with and improve the data and they use it.

Applying AI in Asset Management Distribution

Overcoming Barriers to Adoption

While the potential benefits of AI are compelling, asset managers may face challenges in adopting this technology. Common concerns include data privacy, security, cost, and talent acquisition.

To address these barriers, asset managers should focus on the following:

• Data privacy and security: Implement robust data protection measures and comply with relevant regulations. Partner with AI providers with a strong track record in data security.
• Cost-benefit analysis: Evaluate the potential return on investment (ROI) of AI initiatives. Prioritize projects with clear business outcomes and measurable benefits.
• Pick the right Partners: Beware of legacy firms rebranding their old businesses without a clear competency in AI or modern data technologies.

By proactively addressing these challenges, asset managers can mitigate risks and maximize the value of AI investments.

Conclusion

The asset management industry faces a pivotal moment. Traditional CRM systems, designed to analyze past data, are struggling under the weight of today's vast and complex data landscape. These limitations stifle growth and innovation, making it clear that change is needed. Data unification combined with supervised AI offers a transformative solution, enabling organizations to pivot towards sustained growth. By leveraging AI, asset managers can fully unlock the potential of their data and enhance the value of their distribution assets.

Though implementing AI requires careful planning and investment, the long-term advantages are significant. Overcoming initial challenges and adopting this technology will position asset management distribution teams for success in a rapidly evolving environment. The future of asset management lies in harnessing data-driven insights and intelligent automation. It's time to view AI as a strategic catalyst, propelling organizations towards a data-centric, and data-driven, future.

– Charlie Shaffer, President & CRO, TIFIN AMP