Predictive Analytics Capabilities for Wealth Management Companies
Being technologically advanced and keeping pace with the latest market trends have become a key differentiator. It gives businesses the competitive edge when it comes to delivering a better customer experience and increasing operational efficiency and ROI. Wealth management companies are no exception. When it comes to identifying risks and opportunities based on data, predictive analytics comes in handy. It helps wealth management companies identify potentially outperforming equities, makes new forms of data analyzable, automates decision-making processes, and reduces the negative impact of human biases on investment decisions, etc. In this article, we talk about the current state of predictive analytics in the wealth management sector, its future, and use cases from our experience.
Today of Predictive Analytics
Predictive analytics is a branch of advanced analytics that uses machine learning, data mining techniques, and historical data combined with statistical modeling to make predictions about future outcomes. The use of predictive analytics for wealth management serves a variety of purposes, but in short, it is always about finding patterns in data and identifying either risks or opportunities. Let’s find out what impact predictive analytics drives for wealth management.
Predictive analytics in wealth management derive relevant information that gives advisors insight into their clients’ current and potential financial needs. For example, investment advice can be tailored based on social signals indicating major life events, such as the birth of a child or retirement.
In today’s competitive environment, personalized engagement can work wonders. Clients have specific preferences on how they want to be served and communicate with their advisors. Using predictive analytics, you can forecast which forms of communication (email, mail, SMS, or phone calls) will be most effective in targeting specific clients and what frequency of communication is optimal for them.
Smooth Digital Onboarding
With AI-driven advising, the client’s current life stage, interests, and portfolio goals are analyzed, the most relevant starter products are recommended, and AML and KYC checks are performed in the background by pulling data from internal and external sources. Instead of a lot of paperwork, clients are impressed with straightforward, interactive digital onboarding processes.
Getting Actionable Insights
Predictive analytics is used to tap into the power of both quantitative and fundamental investment analysis. They help analyze vast amounts of structured and unstructured financial and market data that drive intelligent wealth management advice. For example, AI can help manage share portfolios and predict whether it is time to buy more stocks.
Helping Advisors Be More Efficient
Artificial intelligence recognizes typical occasions when an advisor contacts a client, such as a change in the portfolio or address, birthday wishes, or some significant life events. In these cases, clients can be contacted automatically. Wealth management AI tools provide advisors with interactive client dashboards with actionable insights and information about the client portfolio, including performance against benchmarks. This way, advisors save a lot of time on mundane tasks.
Ensuring Your Compliance
Wealth management companies have to attain optimized investment returns while maintaining compliance. With AI-driven algorithms providing insightful content to financial advisors, it is critical that these recommendations are made within the bounds of internal policies and external regulations. This requires the ability to quickly apply the right policy constraint for the right context. AI-powered compliance management sorts through this complex web of regulations, doing so at the speed clients and their financial advisors need.
Enhancing Risk Management
AI can not immediately make all advisors play fair and square, but it enables minimizing potential misconduct and risky behavior. Compliance requirements for wealth management have become more stringent, that’s why to eliminate all potential regulatory and security issues, it’s important to monitor trading and review all transactions for concerns. AI allows setting up a variety of triggers for unwanted scenarios and facilitates transaction screening helping to prevent financial crime.
Tomorrow of Predictive Analytics
The future of wealth management companies is currently being shaped by digital and AI. Below we describe the domains where predictive analytics will be applied.
Enriching Systems of Record with Systems of Engagement
Systems of record are the applications that help to run a business and have information about accounts, transactions, owners, holdings, price/cost basis, performance, beneficiaries, financial plan, stated goals, etc. This data has always been the primary source of client information and the basis for creating investment plans or clients. However, these systems alone can’t generate enough insights for more strategic purposes, such as building analytics to predict or respond to client behavior (e.g., next best action, predict attrition, propensity to buy, etc.). That’s why they should be integrated with the systems of engagement that unite CRM systems, call center interactions, web/mobile interactions, different profiles (spending, risk, professional, health), social media, insurance, etc.
Building Learning Loops
Learning loops are algorithms that enable more personalized experiences and predictive actions. They begin with an insight, lead to an advisory conversation, recommendation, and track client behavior afterward. These loops have several benefits:
- Capturing financial and experience data, structured and unstructured, which helps to build deeper client context.
- Analyzing that data via AI and machine learning.
- Deriving insights that have predictive or business value.
- Suggesting future actions to optimize the client experience.
Building such algorithms empowers advisors to have more meaningful client conversations that improve over time.
Streamlining Client Reporting
If your back-office processes are tangled and fragmented, you can’t provide transparency on costs, fees, and trades, which is a central requirement both among customers and regulators. Robotic process automation (RPA) helps to streamline data management and reporting. Machine learning, in its turn, allows you to move away from simple automation and rule-based report generation and toward on-demand custom reporting.
Enriching Operational Insights
The more data points you supply to your proprietary AI algorithms, the more accurately you predict where your customers want to be during their next life stage. These data points include:
- Crowdsourced data about demographics, economics, and social status that helps to pin microeconomic and macroeconomic trends to customer needs and behaviors.
- Alternative FinTech data displaying the picture of a customer’s credit standing, and spending/ savings habits.
- Customer social media sentiment around the brand and estimate general market trends for investing advice.
Predictive Analytics Use Cases
There are multiple areas in fintech where AI and predictive analysis can come in handy — let’s take a closer look at some use cases. Itexus has been delivering AI solutions since 2013, so we are going to give you our portfolio examples:
💡 AI-based Financial Data Management Platform
For an innovative fintech company from South Korea, we delivered the frontend part of a custom financial data management platform that automates key-decision making processes with AI-based predictive modules reflecting the credit cycles. For this, the platform pulls large amounts of financial information from the customer’s database and visualizes it. Itexus enabled fast and seamless data transfer from the client’s API, which had been generating data in the JSON interchange format, to the interface where the collected data had to be comprehensively visualized in multiple forms, diagrams, and charts.
💡 AI-Powered Financial Analysis and Recommendation System
The system uses machine learning techniques to process content feeds in real-time and boost the productivity of a financial analyst or a client relationship manager in different domains, including wealth management. The system’s functionality includes investment portfolio analysis and optimization; fund recommendation based on quantitative analysis and backtesting; content recommendation; client prioritization based on client’s portfolio, transactions, CRM notes, and market events analysis; real-time analysis of multiple data feeds; etc.
💡 Wealth Management Platform with Robo-Advisor, Remote Portfolio Construction, and Monitoring Functionality
The platform connects investors with a professional wealth-advisory company. Investors can answer a questionnaire and receive either a recommended model portfolio or a custom-tailored individual portfolio. It is then monitored, rebalanced, and adjusted by a professional wealth advisor based on the changing market conditions and the client’s goals.
💡 Investment Management Platform
This private investor portal is equipped with an automated aggregation of financial data and visualization tools. It provides investors with a well-organized summary of the performance of the chosen pre-IPO companies at the seed and early stages. We enriched the solution with complex business logic that provides importing, aggregating, and visualizing the content from the custom-built CMS and third-party services like Backstop and Dropbox.
To Sum Up
AI-powered predictive analytics is the present and future of the wealth management sector. It offers proactive and accurate recommendations, enables faster decision-making, and improves governance. AI-based analytics solutions work with both unstructured and structured data to predict and recommend the next best course of action. If you want to leverage the power of AI in your solution, reach out!
Originally published on Itexus Blog on January 4, 2022.