To move from data consumption to value creation, several enablers must be in place. These span technology, process, people, and ecosystem. 

Real-time data integration: from siloed systems to single source of truth 

A foundational step is creating a ‘single source of truth’ for client, portfolio, market, and risk data. This means eliminating fragmented systems, integrating front, middle and back offices, ensuring data timeliness and consistency. Real-time or near-real-time data access is key — it empowers advisers and systems to respond quickly to market movements or client signals.  

Having a trusted data backbone also supports advanced analytics and AI. When an AI model is fed stale, inconsistent, or poorly governed data, it can produce unreliable outputs and misleading insights — increasing the risk of poor advice, erroneous actions, and ultimately undermining adviser trust and client outcomes. 

• Practical AI use case: A wealth firm builds a real-time data ingestion pipeline that integrates client transactions, market movements, news sentiment feeds, and adviser-activity logs into a central platform. A machine learning model continuously analyses these inputs — not only to detect early signs of client disengagement (such as reduced portal logins, shifts in risk profile, or large outflows) — but also to interpret how market developments and sentiment may be influencing each client relative to their investment objectives.

When potential risks or opportunities are detected, the system alerts the adviser and generates personalised talking points aligned with the client’s financial plan and current market context. Equipped with this insight, the adviser can re-engage the client in a timely, relevant conversation that builds trust and drives meaningful action — improving both client retention and adviser productivity. 

AI and ML: examples of personalisation, risk detection, portfolio optimisation 

Once the data foundation is established, firms can deploy AI/ML to unlock greater value. Examples include: 

Personalisation: Using client-behavioural, demographic and transaction data to recommend tailored products, content or services. 

• Use-case: An AI-driven ‘next-best-action’ engine identifies that a younger mass-affluent client has shown interest in thematic ETFs (detected via clicks and holdings) and prompts the adviser to offer an ESG-thematic investment vehicle — increasing cross-sell and engagement potential.  

Risk detection: Identifying early warning signs of client churn, portfolio drift, over-concentration, or behavioural risks (such as increased risk-taking in volatile markets) — especially when these factors deviate from a client’s investment policy statement, financial plan, or long-term retirement goals. 

• Use-case: A risk-analytics model monitors portfolio holdings and detects that a HNW client has more than 60 percent exposure to a single sector and has recently reduced trading activity. The system alerts the adviser with a ‘rebalance suggestion’ and a client-communications script. This proactive approach reduces concentration risk and strengthens the client relationship. 

Portfolio optimisation: Leveraging ML models to optimise allocations across multi-asset portfolios, identifying inefficiencies or correlations that traditional methods may miss. 

• Use-case: Using alternative data (news sentiment, social media signals) from LSEG’s MarketPsych Analytics, a wealth manager builds a model that adjusts multi-asset portfolios for forward-looking sentiment shocks. Clients who adopt the model see a 2–3 percent improvement in returns versus traditional benchmarks. 

LSEG’s ‘Mastering wealth management: The quantitative modelling advantage' describes how LSEG’s StarMine models combine quantitative research, AI, and sentiment analysis to drive alpha and manage risk. 

Digital platforms: onboarding, CRM, mobile-first tools that empower clients 

A further enabler is the digital platform layer — comprising mobile apps, adviser portals, onboarding flows, CRM systems, and client portals. These platforms allow data and analytics outputs to be surfaced in a way that engages clients and advisers. 

• Use-case: A client onboarding process uses AI-driven identity verification, automated know-your-customer (KYC) screening (via vendor APIs), a risk-profiling chat-bot, and immediate customised investment-journey design. The system automatically segments the client, proposes onboarding content, and pre-populates portfolio suggestions. Time-to-onboarding drops significantly, conversion improves, and adviser time is freed up to focus on more value-adding activities. 

LSEG’s Client-Centric Experiences offering highlights the LSEG Market Answers AI-chatbot and widget capabilities, enabling wealth firms to deliver personalised digital portals quickly. 

Cloud adoption: scalability, resilience, agility  

Modern analytics and AI demand scalable compute, flexible storage, and agile deployment. Cloud adoption is increasingly a prerequisite. By migrating from on-premise, monolithic systems to cloud-native or hybrid architectures, firms gain resilience, speed to market, and cost efficiency. 

• Use-case: A wealth manager migrates its portfolio-analysis and adviser-dashboard stack into a cloud environment, enabling real-time data refresh, elastic compute for AI-models during market stress, and API-first access. The firm is then able to generate client-onboarding modules in significantly less time than before. 

Ecosystem partnerships: APIs, FinTech collaborations, vendor selection 

No firm builds everything internally. The shift to value creation often requires a broader ecosystem: third-party FinTechs, data vendors, analytics-platform providers, and open APIs. Wealth managers must choose vendors that provide not only data but analytics, insight engines and developer-friendly platforms. 

• Use case: A wealth management firm integrates LSEG’s wealth-data-APIs, a FinTech analytics engine (for behavioural signals), and a CRM platform. The result: advisers receive triggered ‘next-best-action’ prompts in the CRM based on real-time client and market signals, boosting cross-sell by 15 percent within six months. 

According to industry research, 81 percent of asset & wealth management organisations are exploring strategic partnerships or ecosystem builds to enhance tech capabilities. 

Measuring ROI: beyond cost savings  

Often, firms embark on analytics or AI programmes with an expectation of cost savings (such as fewer manual processes, lower headcount, or reduced error). Although cost efficiency is important, it should not be the only metric. Firms that win are those that define value in a broader set of business-outcome metrics. 

How leading firms define value from AI investments 

Leading wealth managers adopt metrics such as: 

• Client retention and loyalty: leveraging analytics to identify at-risk clients and pre-empt churn. 

• Client engagement: measuring the frequency, relevance, and quality of client interactions (through digital and adviser channels). 

• Cross-sell/upsell rates: insights that identify next-best-product or service tailored to client life-stage, risk-profile, or behavioural signals.  

• Time-to-market: how quickly a new digital client experience, product, or insight-engine is deployed. 

• Adviser productivity: measuring time saved on adviser preparation, proposal generation, client segmentation, and insights provisioning.  

• Net Promoter Score (NPS) or other client-satisfaction metrics: improved experience leading to referrals, lifetime client value, and reduced attrition. 

For example, LSEG research has shown that 62 percent of wealth management firms believe AI will significantly transform their operations; 68 percent of investors expect digital experiences to match the leading technology.  

Example metrics in practice 

• Time saved: Wealth firms deploying AI-powered proposal engines and workflow automation tools have reported significant reductions in adviser preparation time — in some cases cutting administrative effort by up to 30 – 40 percent, according to industry analyses (e.g. Deloitte, 2024; Capgemini World Wealth Report, 2024). 

• Adviser productivity: Firms that embed data-driven analytics into client-review workflows typically see notable gains in adviser capacity — enabling more frequent and higher-quality client interactions each quarter (EY NextWave Wealth Management, 2023). 

• Engagement: Personalised digital experiences, such as tailored insights and portfolio updates, have been shown to increase client portal engagement and digital adoption, improving satisfaction and deepening relationships (LSEG Insights: Four impactful approaches to boosting client engagement in wealth management, 2024). 
• Client retention: Predictive analytics that identify clients at churn risk allow advisers to intervene earlier, with firms reporting material improvements in client retention rates following targeted re-engagement campaigns (Accenture Wealth Management Trends, 2024). 
• Cross-sell: “Next-best-action” and “next-best-product” analytics have driven measurable uplifts in share of wallet and product penetration across client segments, as noted in McKinsey’s State of AI in Wealth and Asset Management (2023). 

• Client satisfaction: Wealth managers adopting AI-enabled personalisation and streamlined adviser workflows often see meaningful gains in Net Promoter Score (NPS) and client advocacy (Capgemini World Wealth Report, 2024). 

These kinds of outcome-focused metrics help shift the conversation from operational efficiency (“we saved X in costs”) to client-centric impact — demonstrating tangible improvements in engagement, retention, advice quality, and lifetime client value. 

About the WealthTech Insight Series (WTIS)

This research-led white paper is part of The Wealth Mosaic’s WealthTech Insight Series (WTIS), an ongoing research series focused exclusively on technology in the wealth management sector across the world.

Rather than a one-off research process, the WTIS will seek to build an ongoing program of research among wealth managers of different types across the world on a broad range of technology and related topics, building up an aggregated knowledge base of both qualitative views and perspectives as well as quantitative data points.

Discover our white paper collection!

• Managing model portfolios on multiple platforms – read here
• Productivity and growth in wealth management – read here
• The quest to become the best – becoming the trusted wealth coach and adviser – read here
• The role of technology for recruitment and retention within wealth management – read here
• From survival to reinvention: the new playbook for technology spend in wealth management – read here

About The Wealth Mosaic

The Wealth Mosaic is a UK-headquartered online solution provider directory and knowledge resource, focused specifically on the wealth management industry.

For wealth managers, the buy side of our marketplace, The Wealth Mosaic is designed to enable discovery of key solutions, solution providers and knowledge resources by specific business needs.

For solution providers and vendors, the sell side of our marketplace, The Wealth Mosaic exists to support the positioning, exposure and business development needs of these firms in a more complex and demanding market.

Discover our latest reports!

• US RIA Toolkit 2026 – read here
• Future View Toolkit 2025 – read here
• UK Toolkit 2025 – read here
• European WealthTech Landscape Report 2025 – read here
• AI Toolkit 2025 – read here
• Client Experience Toolkit 2024 – read here

Join our community and follow us on LinkedIn here.