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Artificial Intelligence (AI) in Investments and Wealth Management
ScienceSoft relies on 35 years of expertise in artificial intelligence and 19 years in investment software development to design and build robust AI solutions for wealth management and investments.
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Investment and Wealth Management AI: The Essence
Artificial intelligence for investments and wealth management helps domain professionals accurately predict capital market dynamics, spot profitable investments in seconds, and instantly enforce the optimal trading steps, maximizing portfolio returns and minimizing financial risks.
When used in investments, AI brings up to 70% reduction in financial data analysis costs, more than 14% improvement in investment managers’ productivity, and a ~30% decrease in investor support expenses.
Financial institutions that employ AI in wealth management can expect an 8%+ increase in assets under management and over 7% growth in revenue.
AI in Wealth Management and Investments: Market Info
The global market of AI for asset management was valued at $2.61 billion in 2022 and is projected to reach $17.01 billion by 2030, growing at a CAGR of 24.5%. Investment and wealth management are among the largest use case segments for AI in asset management.
According to a recent report by Accenture, 98% of financial advisors believe AI has a transformative impact on the wealth management industry and will reshape the way financial advice is created for, delivered to, and consumed by clients in the near future. The main driver for the popularity of AI-powered investment and wealth management solutions is the increasing demand for fast, accurate, and efficient analysis of high-volume investment data, optimized portfolio performance, and improved investor experience.
How AI for Investments and Wealth Management Works
Main use cases
Investor outreach
Demand analytics to identify potential customers and better understand investor needs. Recommendations on winning investment offerings and outreach channels for various target audience segments to attract new clients.
Investor interaction
AI-enabled virtual assistants to handle simple investor communication tasks and provide instant 24/7 support. It helps improve wealth managers’ productivity and drive high customer satisfaction and engagement.
Investor onboarding
Comprehensive analysis of investors’ identity, solvency, and risks to prevent illegitimate access to trading and wealth management services.
Service personalization
Suggestions on the best-performing portfolio structures and investment strategies for each customer to meet investors’ individual opportunities and goals.
Intelligent investment advisory
Real-time prescriptions on the next best actions for wealth managers and investors to maximize financial gains from trading operations.
Trading automation
Intelligent automation of trading activities to instantly respond to emerging market signals and eliminate low-value manual efforts.
Portfolio management
Analytics-based portfolio planning, diversification, and optimization to maximize investment profitability and minimize risks.
Investment security
Real-time detection of fraudulent investment patterns to timely take protective measures and prevent financial losses.
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NB: Wealth management AI can also be helpful for administrative use cases like demand prediction, financial advisor task planning, and service price optimization. |
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Architecture
ScienceSoft creates AI-based investment solutions with scalable and secure architecture that enables smooth intake and analysis of financial big data. Below, our consultants share a sample architecture of AI software for wealth management and investment, describe its key components and supported processes.
- Uploading investment-relevant data from the available sources to the data lake for storage and its instant transfer to the advanced analytics system for processing.
- Data preprocessing (sorting, filtering, enrichment, and cleansing) to improve its quality. Storing the structured, ready-to-analyze data in the data warehouse.
- AI model design, training, and tuning in the model management module (operated by data scientists).
- Analyzing investor profiles, portfolio performance, and capital market movements using an ML model. The model forecasts investor behavior and asset prices, prescribes portfolio optimization steps and profitable trading actions.
- Exporting analytical results to the analytical database for storage and to the model management module for model self-training.
- Communicating AI-based insights and AI-prescribed investment decisions to the relevant systems. Wealth managers and investors can access the analytical results via role-specific investment apps.
AI-based software integrates with:
- A wealth management company’s corporate systems (e.g., portfolio management software, CRM, an investor portal, an accounting system) and communication channels (email, messengers, etc.) to import relevant historical information and share investment-related data and decisions.
- Third-party data sources (financial data marketplaces, credit rating bureaus, banking systems, etc.; treasury software of institutional investors) to obtain available investment-relevant information.
- Trading platforms to automate asset trade execution.
Key features
Real-time investment data processing
Deep learning, image analysis, and natural language processing technologies enable real-time aggregation and processing of multi-format, multi-channel investment-relevant big data: investor documents, asset prices, stock indices, financial news, trading order heat maps, graphic chart patterns, regulatory compliance rules, and more.
Predictive capital market analytics
AI continuously monitors the asset price fluctuation patterns, FX, interest, yield, and inflation rates, on-balance volumes, and other capital market performance indicators. It reveals dependencies between the indicators, predicts the market moves, considers investor sentiment, and delivers insights into emerging opportunities.
Investment portfolio planning
AI analyzes investors’ income levels, financial sustainability, risk appetites and tolerances, profitability goals, taxes, and investment histories. It suggests the proper portfolio composition (baseline asset allocation, investment duration, exposure limits, etc.) for each investor to maximize financial gains.
Investment strategy creation
AI drafts go-to investment strategies and plans taking into account user requirements. Based on the AI-generated suggestions, wealth managers can create tailored investment roadmaps for their clients, and investors can self-design trading journeys.
Portfolio optimization
AI enables real-time calculation and forecasting of all necessary performance metrics (total returns, exposures, weighted yields, average daily balances, value at risk, etc.) at the portfolio, investment, and asset class levels. It instantly spots potential risks and prescribes the optimal steps to balance and hedge the portfolio.
AI-supported trading execution
AI analyzes live trading events, detects chart patterns and price change directions, and provides personalized investor guidance on the next best actions. AI for trading automation mimics investors’ behavior and can initiate position entering, adjustment, or exit when patterns signal the price trend shifts.
AI-guided customer acquisition
AI identifies the needs and preferences of target customers based on the analysis of the existing clients’ portraits, prospects’ web search histories, social media activities, and more. It suggests the investment products, outreach channels, and promotional messages that will most likely resonate with each target audience segment.
Automated investor communication
AI-enabled virtual assistants (chatbots, digital humans) instantly process investor queries and provide real-time responses in a human-like manner. They can automatically communicate portfolio performance to investors, request missing data and documents, inform clients about new investment products, and more.
Intelligent KYC/AML verification
AI validates the identity, income, and risk data provided by investors against the data from third-party sources (e.g., credit rating platforms, bank systems). It qualifies investors against the geography-based KYC/AML requirements and screens the local sanction lists to avoid serving the blocked individuals and entities.
Fraud and non-compliance detection
AI analyzes real-time activities across investors’ accounts and instantly detects suspicious funds manipulations. It monitors investment compliance with relevant standards (SEC, FINRA, EBSA, FATCA, company-specific policies, etc.) and notifies the responsible parties and regulators about non-compliance cases.
Implement AI to Upgrade Investment and Wealth Management
ScienceSoft is ready to design and build a tailored AI investment solution that drives tangible improvements in wealth management efficiency, enables new financial advisory models, and ensures top-notch investor experience.
Explore ScienceSoft’s Featured Success Stories
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Techs and Tools We Use to Build AI-Powered Investment Solutions
Addressing the Challenges of AI in Investments and Wealth Management
While 88%+ of wealth management service providers are pursuing AI initiatives, many still doubt AI’s ability to address certain operational and compliance aspects of investment and wealth management. Having decades-long expertise in AI development and investment digital transformation, ScienceSoft knows how to handle potential issues in a safe and pragmatic way.
Challenge #1: Insufficient trustworthiness of AI predictions
In the wealth management space, poor investment decisions can cause huge financial and reputational losses. It poses extremely high requirements for the accuracy of AI-delivered insights used for investment planning and hedging. The reality is that even ~80% precision of AI predictions can be hard to achieve.
Make sure you have a scalable and high-performing data management infrastructure. Being the cornerstone for deriving high-quality data from all the required sources and fast (often real-time) processing of this data, a robust data infrastructure lays the groundwork for reliable AI outputs. Our team can advise on the necessary infrastructure improvements and aid in establishing an AI-ready data management framework to ensure the smooth and accurate functioning of your intelligent investment solution.
Challenge #2: Opaque AI's decisioning logic
Wealth managers have a fiduciary responsibility to their clients and are legally obliged to provide a clear rationale behind their investment decisions. In case of AI-prescribed investments, achieving full transparency of decision-making logic may be a challenge, since most intelligent engines rely on highly sophisticated algorithms that are a ‘black box’ even for AI experts.
ScienceSoft’s AI Clients Share Their Experience
ScienceSoft’s team undertook the development of our AI-based software product from scratch and showed deep expertise in the .Net environment and AWS and Azure services. They delivered software on time and with the required quality. The team adhered to consistent communication and was proactive in offering beneficial product customizations for our clients.
ScienceSoft was to cover the end-to-end development of an intelligent invoice processing module for our product. The team used the microservices approach to ensure the system’s scalability, which is critical for us as our user base is growing. We were particularly pleased with neat alignment of the developed module with our requirements.
ScienceSoft’s team identified core errors that prevent efficient solution operations and implemented high-speed convolutional neural networks to fix them. They promptly developed new software functionality fully adhering to our requirements. We are satisfied with our cooperation with ScienceSoft and their skilled development team.
Costs of AI-Powered Software for Investments
Based on ScienceSoft’s experience, building a custom artificial intelligence solution for wealth management and investments may cost around $100,000–$650,000+, depending on the solution's complexity.
The major factors that affect the development budget and timelines are:
- The solution’s feature scope.
- The number and complexity of AI models.
- Performance, scalability, security, and compliance requirements for the software.
- The number and complexity of integrations.
- Requirements for the UX and UI of the investment application.
- The required development scope (MVP, a fully-featured solution).
- The sourcing model and team composition.
Here are some examples of ballpark estimates:
$100,000–$250,000
An AI-based investment solution that relies on non-NN ML models to process data from 1–3 internal sources and delivers analytical output in batches.
$250,000–$450,000
AI-based software that employs NN models to process investment data from corporate and third-party sources and deliver intelligent predictions.
$450,000–$650,000+
A complex AI-powered predictive and prescriptive system that uses advanced NN models for real-time processing of investment data from 5–10+ proprietary and third-party sources.
Investment AI Consulting and Implementation by ScienceSoft
In AI implementation since 1989 and in investment software development since 2005, ScienceSoft delivers reliable AI solutions for investment and wealth management. We ensure that AI project goals are met within the agreed budget and timeframes and that uncertainties are addressed agilely.
Consulting on AI for investments
Our consultants create a detailed feature set, architecture design, and tech stack for your AI solution and advise on security and compliance. You also receive a step-by-step roadmap for smooth and predictable AI implementation.
Implementation of AI for investments
With 750+ IT talents on board, we take charge of every development step, from ML model design and training to AI software integration with the required systems. You get a high-quality AI solution quickly and at the optimal cost.
About ScienceSoft
ScienceSoft is a global IT consulting and software development company headquartered in McKinney, Texas. We deliver AI solutions for accurate and efficient wealth management and profitable investments. In our AI projects, we rely on robust quality management and security management systems backed up by ISO 9001 and ISO 27001 certifications.
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