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Investment Data Analytics
Software Features, Costs, and Gains
With 35 years of experience in data analytics, ScienceSoft builds custom solutions for all types of investor firms to help businesses make risk-proof investment and portfolio management decisions, optimize operational processes, and streamline reporting to regulatory authorities.
Investment Data Analytics: the Essence
Investment analytics allows investment professionals to drive insights from market and company data and make optimal portfolio planning and management decisions. Investment analytics solutions ensure real-time visibility into market movements and the company's internal operations, forecast potential financial returns, and leverage what-if modeling to assess the impact of business decisions and market events.
Companies and individuals that can benefit from investment analytics
Mutual funds, exchange-traded funds, pension funds, hedge funds, unit investment trusts, investment banks, private equity firms, wealth management and investment advisers.
Asset classes supported by custom investment analytics software
Stocks, bonds, cash equivalents (loans, marketable securities, etc.), alternative assets (e.g., real estate, commodities, derivatives).
Investment management software, investment accounting software, investor lifecycle management software, investor portals, trading platforms, risk management software, capital market data platforms, investment company bank accounts.
Implementation costs: $100,000 – $1,000,000, depending on the solution's complexity.
ROI: up to 240%
Investment Analytics: Key Features
Below, ScienceSoft's professionals outline some of the most common investment analytics features.
Investment planning
- Capital market analytics, including technical and fundamental analysis.
- LLM-supported analysis of web-wide investment sentiment.
- Providing AI-powered stock buying recommendations (e.g., buying stocks if the fair market value is higher than the market price).
- Predictive analytics to forecast financial returns.
- What-if modelling based on the analysis of historical investment performance and the current market situation.
- ML/AI-powered investment prescriptions based on the analysis of an investor's financial data (e.g., income, expenses, assets, and liabilities) and investment goals.
- Automated monitoring of the total return of a portfolio and individual investments, including capital gains, dividends, interest, and distributions.
- Tracking portfolio performance vs. asset-specific benchmark index.
- Factor exposure analysis and identification of return drivers (e.g., interest rates, inflation, company earnings).
- Asset allocation analytics (e.g., what-if models of various asset allocation strategies).
- Regular portfolio rebalancing (e.g., based on risk tolerance, market conditions, tax implications).
- Alerts on reaching predefined limits (e.g., for price, price-to-earnings ratios).
Risks analytics
- Assessing risk-adjusted return through case-specific calculations.
- Standard deviation calculation.
- Risk attribution analysis (e.g., across individual securities, asset classes).
- Continuous monitoring of competitor activity, borrowers' credit scores, trading volumes, bid-ask spreads to detect possible market, credit, and liquidity risks.
- Tracking hedging transactions across various derivative instruments.
- What-if analysis for various risk factors and portfolio rebalancing options.
- Analyzing internal processes to improve operational efficiency (e.g., identifying delays or errors in trade execution).
- Insight into financial management (e.g., financial analytics, (e.g., calculating financial performance KPIs like FCF, ROA, employee compensation vs performance comparison)).
- Analyzing investment managers' performance and identifying the impact their decisions have on portfolio performance.
- Real-time analytics of asset purchasing, selling, reinvestment, switch, split, STP, SWP.
Fraud detection
- AI-supported identification of fraud related to insider trading, pump and dump schemes, HFT manipulation.
- Continuous monitoring of investment accounts to identify attempts of account takeover or theft.
- Immediate alerts on detected suspicious activity.
Compliance analytics
- Monitoring of transactions, client information, trading activities, reporting procedures, advertising and client communication, etc. to support compliance with regulations like SEC, GLBA, OFAC and FINRA, SOX, GDPR, AML/KYC, CMA.
- Alerts on compliance violations.
- Reports for regulatory authorities in compliance with the established reporting forms, e.g., form PF (for private fund advisers), form ADV (for investment advisers), and form CRS (for broker-dealers).
- Automated report submission to regulators.
Tax analytics
- Identifying the most tax-efficient investment funds.
- Assistance in tax-loss harvesting, e.g., by forecasting potential losses in a portfolio, pinpointing to sell underperforming investments.
- Assessing tax-related impact of holding different assets in taxable, tax-deferred, and tax-exempt accounts.
- What-if modeling for tax implications of investment decisions, including incurred tax liabilities, regulations, and efficiency.
- Calculation of incurred tax liabilities under various tax jurisdictions.
(for wealth management)
- AI-powered verification of investor data, including identity, taxpayer identification number, bank account information, and compliance with company-specific eligibility criteria.
- Location-based AML/OFAC, KYC checks for investors.
- Automated client segmentation, e.g., by risk tolerance, investment preferences, financial goals.
- Continuous monitoring of clients' investment activities for timely segmentation refining.
Essential Integrations for Investment Analytics
Investment management software
- To get a 360-degree view of portfolio performance and optimize it.
- To provide insights into funds management and enable its informed improvement.
- To build tax-optimized investment strategies.
Investment accounting software
- To enable accurate investment accounting.
Investor lifecycle management software
- To help build client-specific investment strategies.
- To increase client loyalty and retention.
- To support informed investment decisions of individual investors.
Trading platforms
- To collect investment performance data.
Risk management software
- To timely detect risks and address them in an optimal way.
Capital market data platforms
- To collect data on market performance.
Investment company bank accounts
- To analyze received and due payments and quickly initiate investment activities.
Things to Pay Attention to When Building an Investment Analytics Solution
The specifics of investments you work with
The business-specific insights a company is interested in will determine the solution’s functional core. E.g., if you run a wealth management firm, you’re likely to benefit from personalized investment recommendations, while a pension fund will need stress-testing capabilities to meet its long-term funding requirements.
Granular frequency of data updates
To combine data-driven value and cost-efficiency, it’s crucial to decide what changes you need to react to immediately and what data is worth analyzing from the historical perspective. E.g., you can implement real-time alerts on portfolio reaching a pre-set risk margin, while identifying financial management bottlenecks will certainly require in-depth analysis of past decisions and their outcomes.
Compliance aspects
The analytics solution needs to support compliance with laws and regulations related to data privacy, customer protection, financial management, AML/KYC policies. Firstly, the solution should enable 100% secure data transfer, storage, and sharing. Secondly, it’s worth implementing compliance monitoring and alerting capabilities to detect potential problems before they become major issues.
Convenience of data exploration and reporting
A solution needs to produce flexible dashboards that can be easily tailored to different user groups. E.g., portfolio managers are likely to appreciate customizable stock watchlists, while marketing professionals may need flexible customer segmentation tools. It’s also a good practice to create custom reporting forms, e.g., PF (for private fund advisers), ADV (for investment advisers), CRS (for broker-dealers) and thus enable automated submission to authorities.
Highlights of ScienceSoft's Investment Analytics Portfolio
Costs & ROI
Investment analytics software implementation may cost from $100,000 to $1,000,000+, depending on software's complexity.
On average, data analytics in investment brings a three-year ROI of up to 240%. The main ROI drivers include real-time visibility into financial markets, 50% faster regulatory reporting, and personalization insights.
$100,000–$250,000
A basic solution that:
- Enables analytics across 1–2 business areas, e.g., operational KPIs.
- Integrates with 1 or 2 data sources, e.g., investment management software.
- Processes data according to an established schedule (e.g., every 15 minutes, hour, 12 hours).
- Enables scheduled and ad hoc reporting.
$250,000–$400,000
A solution of medium complexity that:
- Enables analytics across multiple business areas, e.g., operational, financial, portfolio performance KPIs.
- Integrates with up to 7 data sources (corporate and external).
- Processes data both according to an established schedule and in real time.
- Enables ML-powered diagnostic and predictive analytics.
- Enables automatic reports submission to regulators.
$400,000–$1,000,000
An advanced solution that:
- Enables analytics across all the required business areas.
- Integrates with multiple internal and external systems, including blockchain-powered software.
- Enables real-time big data analytics (e.g., for instant KPI calculation, for investment fraud detection).
- Ensures advanced ML-powered root cause analysis and forecasting.
- Provides AI-supported optimization recommendations.
- Enables custom reports compliant with reporting forms like PF, ADV, CRS.
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87% of Companies Saw Measurable Business Value from Their Data Analytics Initiatives
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The 2024 Data And AI Leadership Executive Survey features feedback from more than 100 global industry leaders that implemented analytics solutions. The report includes over 20 renowned investment-related companies, including Fidelity Investments, JPMorgan Chase, Morningstar, The Vanguard Group, and more. |
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Turn to ScienceSoft to Make Your IT Initiative a Success
Our team can build a reliable investment analytics solution that will let you consolidate and evaluate case-specific investment analysis factors like entry price, expected time horizon, macroeconomic situation, and risk tolerance for informed decision-making.
Consulting on investment analytics
We offer full-scope consulting that covers everything from solution conceptualization to its launch and provide professional advice on individual components like ML/AI capabilities, big data techs, or regulatory compliance.
Investment analytics implementation
We can implement a scalable, secure, and high-performing analytics solution. Our goal is to provide you with features and dashboards that are fully tailored to your organization's specifics and make it easy to stay in line with the challenging dynamics of investment business.
What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass mere project administration off as project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
About ScienceSoft
ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 1989, we help companies across 30+ domains build tailored analytics solutions to make data-driven decisions and get reliable insights for capitalizing on market opportunities and mitigating risks. Being ISO 9001- and ISO 27001-certified, we can guarantee top software quality and complete security of our clients' data.