ScienceSoft Global
Investment Management Software
Features, development steps, and costs
ScienceSoft’s clients get custom investment management solutions that address the functional, integration, and compliance constraints of ready-made software. We focus on software logic accuracy from day one, always factor in regulatory rules when designing investment software, and apply flexible modular architectures to create interoperable software that stays relevant for 10+ years.
Investment Management Software: Key Aspects
Investment management software is used as a centralized platform for managing investor relationships, multi-asset portfolios, and multi-brokerage investment activities. Such software automates processes like investment research, portfolio construction, trade execution, risk control, and reporting.
Custom investment management software is a preferred choice for companies that want to orchestrate and automate multi-step workflows across proprietary capital formation, investment operations, and hedging. Custom solutions can be built to support a broad range of asset classes, including alternative investment vehicles like real estate, private equity, derivatives, and crypto tokens.
Investment firms often opt for custom software to leverage tailored analytical and automation features powered by artificial intelligence (AI). Custom systems can accommodate investment AI models trained on the company’s proprietary data. Such AI-powered software delivers accurate insights aligned with the firm’s investment strategy and, at the same time, provides investment managers with greater control over the security and transparency of intelligent operations.
Another advantage of custom investment management software is that it can be integrated with any required systems, including legacy software and innovative fintech tools. Custom solutions can also be designed in compliance with any necessary investment regulations, including local frameworks (e.g., SEC for the US, SAMA for the KSA) and domain-specific rules (e.g., AIFMD directive for the EU alternative funds). Plus, such software can be freely upgraded with new capabilities when the company needs or regulatory rules change.
- Key integrations: investment, brokerage, and trading platforms, financial market data platforms, custodians’ systems, and more.
- Implementation time: 9–15 months on average.
- Development costs: $150,000–$2,000,000+, depending on solution complexity. Use our free calculator to estimate the cost for your case.
ROI for Automated Investment Management Solutions
According to a recent study by Deloitte & ThoughtLab, 50% of investment management firms are getting a high ROI from automating their service processes. Financial planning, risk management, portfolio accounting, and investor reporting are cited as the areas of the highest return.
Traditional and alternative investment managers report the following company-wide gains with the investment software that automates such tasks:
Niche Investment Management Firms That Benefit From Custom Software
Key Features of an Investment Management System
Below, ScienceSoft’s consultants share a list of features that serve as a basis for a robust investment management solution. Our team can engineer an all-in-one system from scratch or develop separate, functional components to upgrade the software you currently use.
- Real-time and batch aggregation of capital market data feeds from the connected systems (asset-specific market databases, brokerage and trading platforms, etc.).
- Automated scraping of investment data from public web sources (e.g., financial news platforms, companies’ websites, blogs, social media).
- Rule-based data segmentation (by asset class, source, geography, etc.).
- Real-time market data screening and instant alerts on user-defined events (e.g., asset price movements, earnings call releases).
- Statistical technical analytics.
Advanced:
- Automated retrieval of research-relevant data points (market stats, tickers, entity fundamentals, investor sentiment, etc.) from gathered documents and public web sources using large language models (LLM).
- LLM-supported data summarization and presentation in the user’s preferred format.
- AI-powered capital market analytics offering suggestions on the high-yield and low-risk assets to invest in.
- Intelligent sourcing of emerging investment opportunities (alternative asset classes, promising startups, rising markets, etc.) for portfolio diversification.
- Customizable investment models with user-defined input parameters: asset preferences, maturity period, financial goals, tax efficiency, and risk limits.
- Automated calculation of model-specific investment metrics (time-framed yield, risk, liquidity, leverage utilization, performance fees, etc.).
- Scenario modeling, what-if analysis, and stress testing for single-asset investments, portfolios, investment strategies (e.g., growth, index, momentum investing), and fund management and exit strategies.
- Side-by-side comparison of various investment options and financial asset management strategies.
- Monte Carlo simulations to evaluate the impact of uncertainty factors like regulatory changes, sanctions, and macroeconomic shifts.
- Automatic conversion of model output into actionable mid- and long-term investment plans.
- (For trading) A paper trading environment for trade strategy simulation and outcome measurement.
Advanced:
- Machine-learning-supported predictive analytics to forecast investment returns.
- Intelligent suggestions on the optimal investment strategies and portfolio structures based on the investor’s profile (capacity, time-framed goals, risk appetites) and market trends.
- Creating custom portfolio structures with user-defined hierarchies, asset types, and performance metrics.
- Setting up financial goals, investment limits, and risk exposure thresholds.
- Analytics-driven construction of multi-asset portfolios.
- Automated aggregation of investment activity data from accounts across multiple brokerages and trading platforms (in real time or on a regular schedule).
- Calculating general portfolio metrics (total value, weighted returns, dividend yield, volatility, etc.) and asset-specific indicators (e.g., alpha and beta for stocks, kurtosis for hedge funds, J-curves for private equity).
- Configurable visual formats for portfolio data presentation (default dashboard view, drill-up and drill-down options, particular visualization techniques like charts and graphs).
- Portfolio performance benchmarking against asset-specific indices.
- Automated performance attribution analytics.
- Portfolio performance forecasting based on historical performance data.
- Alerts on investment performance drifts, asset price spikes, and allocations and exposures that reach preset levels.
- Portfolio automated rebalancing based on user-defined rules.
Advanced:
- ML-powered forecasting of portfolio- and lot-level KPIs.
- Real-time portfolio performance and asset correlation analysis.
- Intelligent suggestions on position right-sizing.
Investment order management
- Interactive dashboards with a real-time overview of investment orders and their execution details (by asset class, period, account, venue, etc.).
- Rule-based investment order opening, confirmation, cancellation, renewal, and closing.
- Configuring target values or acceptable ranges for buy and sell prices, leverage levels, asset quantity, and order period.
- Support for market, limit, stop, GTC, bracket, and other order types.
- Automated order routing to the relevant execution systems (e.g., internal systems of brokerage firms, direct investment platforms, OTC marketplaces), triggered by:
- User-defined schedule (e.g., for TWAP strategies).
- Manual confirmation by an investment manager.
- Algorithmic enforcement, e.g., when an asset price reaches a certain threshold or when an asset holding exceeds a certain share of the portfolio.
- Notifications to investment managers on order execution status (pending, accepted, filled, settled).
Advanced:
- AI-powered order analytics for evaluating market, position, and exposure.
- Intelligent suggestions on optimal asset price, buy/sell quantity, order type, timing, and execution venue.
Investment deal management
- Deal pipeline monitoring and deal progress tracking by status (pending due diligence, approved, rejected, closed, etc.).
- Automated processing of deal and investment proposals submitted by potential investment targets (startups and established businesses).
- Rule-based proposal segmentation (by industry, company maturity, requested funding, investment duration, etc.).
- Automated workflows for initial investment due diligence.
- A collaborative workspace for investment committees to review, qualify, and approve prospective deals.
- A virtual data room (VDR) for secure multi-party sharing and access to deal-related documents (critical for due diligence processes).
- Template-based creation of NDAs, PIMs, and investment agreements.
- Notifications to investment managers about bidding deadlines, scheduled deal negotiations, documents pending approval, and more.
Advanced:
- LLM-supported retrieval and summarization of prospect fundamentals and requested funding details from proposals.
- AI-powered proposal analysis and suggestions on high-yield and low-risk deals.
- Deal document e-signing using digital signatures.
Trading
- Direct multi-broker connectivity for instant trade order placement and exit.
- Real-time monitoring of technical indicators like moving averages, OBV, ADX, RSI, MACD, MFI, Bollinger Bands, parabolic SAR, etc.
- Dynamic trading charts reflecting real-time asset price changes, including line, bar, candlestick, point, figure, and Renko charts.
- Automated recognition of technical patterns (continuation, reversal, bilateral, etc.).
- User-defined rules for algorithmic trading strategies.
- Automated trade execution based on preset strategies.
- Rule-based trading strategy backtesting.
Advanced:
- AI-supported identification of arbitrage opportunities.
- Intelligent order routing to the optimal trading platform based on asset type, platform-specific bid and ask prices and spreads, trade commission, etc.
- AI-powered robo-advisors for answering traders’ questions, executing trade commands automatically, and supporting market-making activities.
Liquidity management
- Calculating and tracking investment-related cash inflows, outflows, and liquidity ratios (current, quick, cash).
- Configuring liquidity limits in accordance with redemption, operational, and compliance requirements.
- Forecasting cash, liquidity, and financing needs based on historical data.
- Monitoring funds availability across investment accounts.
- Template-based creation of capital call notices and payment instructions.
- Scheduled and ad hoc distribution of capital calls to investors and custodians.
- Alerts about upcoming liquidity shortfalls (e.g., investor balances dropping below or exceeding set thresholds).
- (for leveraged transactions) Monitoring loan utilization, calculating due principal and interest amounts, and repayment planning in accordance with preset liquidity limits.
Advanced:
- ML-powered cash flow and liquidity forecasting and instant flagging of liquidity risks.
- Intelligent suggestions on the optimal contingency strategies (e.g., dividend distribution rescheduling, financing options).
Risk management
- Continuous monitoring of investment positions and market trends (price volatility, asset-specific trade volumes, investor sentiment, etc.) to detect emerging risks.
- Calculating and tracking investment risk indicators like standard deviation, VaR/CVaR, MDD, Sharpe ratio, Sortino ratio, R-squared, risk-adjusted return, VIX, and FX rate exposures for multi-currency investments.
- Alerts on asset price spikes, investment performance deviations, and allocations and exposures reaching preset levels.
- Modeling and comparing risk hedging strategies (e.g., portfolio diversification, price limit adjustments, employing options).
- Automated workflows for hedging transactions (buying put options, dynamically reallocating assets based on expected market volatility, etc.).
- Monitoring hedging transaction progress and efficiency.
Advanced:
- AI-powered risk analytics and intelligent suggestions on pragmatic hedging strategies and next best actions.
- Investment transaction data recording in an investment book of record (IBOR) according to user-defined rules.
- Multi-book accounting with the ability to configure book-specific base currencies, accounting frameworks (IFRS, US GAAP, SOCPA, etc.), and accounting methods, e.g., trade-date vs. settlement-date.
- Rule-based portfolio and lot valuation based on up-to-date market prices for portfolio assets.
- Automated calculation of realized and unrealized capital gains, cash flows, accruals, positions, and management fees based on custom formulas.
- Calculating taxes for assets held in taxable, tax-deferred, and tax-exempt accounts based on jurisdiction-specific tax rates, wash sale rules, and regulatory requirements (e.g., TCJA for the US).
- Multi-tier distribution waterfall calculations.
- Rule-based equity recalculation and record adjustment for corporate actions (buyouts, stock splits, M&A, spin-offs, etc.).
- Automated transaction reconciliation and exception handling.
- Automated generation of disclosure and tax filings in compliance with the necessary standards (GAAP, FASB, SEC, etc.).
Advanced:
- AI-supported suggestions on the optimal investment allocations and funds withdrawal timing for tax-loss harvesting.
- Rule-based workflows for investor onboarding, pre-qualification, and profiling.
- Geography-based KYC/AML and OFAC verification for investors.
- Automated investor segmentation, e.g., by risk tolerance, investment preferences, financial goals.
- Configurable dashboards providing real-time overview of investor commitments, deal flows, account activities, and portfolio performance.
- Investor interaction planning and scheduling.
- Automated generation and distribution of investor reports (account statements, portfolio performance snapshots, fee summaries, tax forms, etc.).
- Calculating and tracking the chosen investor-related KPIs (by period, location, investor segment, etc.), such as the number of new investors, the total volume of investment deals, total revenue generated, retention and churn rates, and more.
- Centralized storage for investor data, documents, and investor-manager interaction histories.
- An investor portal providing secure investor self-service options, including tools for registration, investment ordering, investment performance monitoring, and access to reports.
Advanced:
- Automated processing of multi-format investor documents using robotic process automation (RPA) and intelligent image analysis.
- AI-supported detection of inaccurate and forged investor data (identity, TIN, bank account information, etc.).
- Automated drafting of investor prospectuses, agreements, memos, notifications, and non-standard reports using generative AI.
- Centralized storage for investment data and documents.
- Automated integration of investor, portfolio, transactional, and market data from diverse sources.
- Rule-based workflows for investment data validation, cleansing, unification, enrichment, segmenting, and lineage.
- Automated coding of investment instrument, issuer, investor, and currency names using investment-specific conventions (e.g., ISIN, CUSIP, ISO 4217) and the investment firm’s custom identifiers.
- Investment document indexing and version control.
- Investment data presentation in the chosen formats (hierarchical structures, directory-based views, dynamic dashboards, etc.).
- Data search by filters, tags, and metadata.
Advanced:
- AI-powered pre-processing, validation, classification, and indexing of unstructured data (investor documents, visual content, media recordings, etc.).
- LLM-supported natural language search of investment data and documents.
Data security and operational compliance
- Role-based permissions to view, edit, and share granular investment data.
- Multi-factor authentication, including location-based, certificate-based, and biometric authentication.
- Encryption of stored and transferred investment data.
- Automated workflows for data backup and recovery.
- Automated investment data deletion according to preset retention and deletion rules.
- Full audit trail of user activities.
- Monitoring compliance of investment operations with AML/CFT, OFAC, SEC, FINRA, GLBA, IFRS, SAMA rules, and more.
- Rule-based detection of non-compliant activities.
- Notifications to the responsible specialists about compliance breaches.
Advanced:
- Intelligent user behavior analytics (UEBA) for real-time investment fraud detection (e.g., insider fraud, embezzlement, Ponzi schemes, churning).
- Investment data hashing, timestamping, and recording in an immutable blockchain ledger.
Important Integrations for Investment Management Software
- For streamlined investor account management
- For faster order placement and execution.
- To track order execution progress and analyze investment outcomes.
- To instantly capture market signals and capitalize on short-term opportunities.
Financial market data platforms
E.g., Bloomberg, Morningstar, FactSet
To access capital market data required for investment research, fundamental and technical analysis, portfolio valuation, and risk prediction.
NB: Investment management software can also be integrated with asset-specific databases relevant to the firm’s investment profile, e.g., sector-specific business databases for private equity firms and real estate databases for REITs.
Internal systems of custodians and fund administrators
- To speed up the replenishment of funds across brokerage accounts.
- For streamlined reconciliation of IBOR records against custodians’ records.
- For quicker aggregation of investor data and investment orders.
- To instantly share investment performance details, personalized offers, documents, and reports with investors.
Steps to Create Robust Investment Management Software
Below, ScienceSoft’s development team shares a high-level plan and best practices for creating reliable investment management software with an optimized TCO.
1.
Engineering software requirements
Investment IT consultants analyze the investment firm’s servicing processes and business needs and elicit requirements for the new investment management software. Based on the collected information, consultants compose a software requirements specification.
- Consider interviewing target business users of the solution (portfolio managers, account managers, investment analysts, etc.) alongside the firm’s project stakeholders. This step helps better grasp the employees’ daily operational needs and reveal non-apparent bottlenecks that may require specialized software features for improved workflow efficiency. For example, applying this approach in ScienceSoft’s investment management software project for a family office helped our consultants uncover the team’s persisting troubles with data reconciliation. As a result, we prioritized implementing auto-reconciliation functionality in the MVP, so that the client can reap productivity gains as soon as the solution is set live.
- By defining compliance requirements for the investment management solution at this early stage, you ensure compliant software design from the outset. Compliance consultants at ScienceSoft analyze and map both the relevant data protection standards (e.g., GLBA, NYDFS, GDPR, SOC 2) and domain-specific operational regulations (SEC, FINRA, SOX, SAMA, MiFID II, etc.).
- With detailed documentation, it’s easier for consultants to communicate their assumptions to the investment firm’s stakeholders and keep the development teams aligned on the project’s goals and constraints. If you are looking to implement systemic documentation processes to drive transparent and consistent teamwork across the SDLC, explore how ScienceSoft approaches documentation.
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2.
Technical and UX/UI design
This stage covers processes like business logic design, architectural design, UX and UI design, and tech stack selection for the investment management system.
- All-in-one investment management software offers vast opportunities for business logic reuse. For example, the same risk assessment models can be utilized in portfolio analytics and compliance checks, balance formulas can be applied for both institutional and retail accounts with minimal modification, and back-office investment ordering logic can be abstracted and reused in the investor portal dashboards. Reusing logic components where possible is how ScienceSoft builds up investment software accuracy, reduces development efforts, and minimizes risks to logic integrity in future evolution activities.
- Most investment management systems would benefit from modular architectures (SOA, microservices, modular monolith) that support logic decoupling and reuse and enable independent component development, scaling, and upgrading. For investment firms that face heavy regulatory scrutiny, like broker-dealers and hedge funds, architects at ScienceSoft suggest choosing a modular monolith architecture for streamlined data management processes. For software covering trading features, a monolith architecture that offers minimized latency and the quickest data exchange would still work best. A win-win option here would be to apply a hybrid architecture, where a trading module is built into a monolith and the rest of the system is designed using flexible, modular patterns. Check how ScienceSoft’s architects select the best approach for each particular case.
- Investment software tech stack is one of the primary points for cost optimization. By using low-code platforms (e.g., Microsoft Power Apps), ready-made logic building blocks (e.g., QuantLib formulas, Spark’s ML components), OOTB UI components (e.g., TradingView’s pre-built charts), and reusable deployment scripts, you can cut the share of costly custom coding and speed up development.
Just like functional needs, requirements for the UX and UI of investment management software vary significantly among various user groups. For example, investor relationship managers value clean aggregated views with slice and dice capabilities, portfolio managers benefit from comprehensive dashboards with rich data visualization options, and investment accountants prefer minimalistic, spreadsheet-like screens. Tailoring user journeys, navigation, layouts, and data density for each investment team role is critical to fostering operational efficiency and accuracy at scale. At ScienceSoft, we apply adaptable layouts (customizable widgets and dashboards, context-aware interfaces, configurable modal windows, etc.) to accommodate different user needs within a single platform without inflating design efforts.
3.
Project management
Project managers map the scope of project tasks, define the resources needed to deliver investment management software, and estimate project timelines and budget.
- Engineering a fully-featured investment management system is a lengthy and demanding project that deals with a large degree of uncertainty. Applying Agile project management methodologies will let you speed up development, flexibly scale the teams when needed, and quickly adapt to changing requirements. 90%+ of ScienceSoft’s investment software projects were led under Agile frameworks and were delivered within the original time and budget agreements.
- A pragmatic risk mitigation plan is a must to prevent project disruptions and investment management software depreciation during development. PMs at ScienceSoft proactively plan responses to both known and potential risks, including regulatory changes that affect digital investment operations and shifting investor preferences, which may entail changes in functional and UX/UI design.
- Mapping structured policies for change request processing and scope extension before development will safeguard you against scope creep and inflation of delivery timelines. In one of our recent projects, an investment regulator failed to organize these processes properly, which led to uncontrollable scope creep. Discover what scoping and change management practices ScienceSoft’s PM consultants applied to get that problematic project back on track.
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4.
Development and testing
At this stage, developers code the back end of investment management software (including specialized components like ML models for portfolio performance forecasting), create user interfaces, and set up scalable data storage.
- Implementing DevOps (CI/CD, container orchestration, etc.) helps speed up development, testing, integration, and deployment operations. It also eliminates manual errors and minimizes regression risks in production releases. For intelligent investment management solutions, engineers at ScienceSoft additionally employ AI-specific DevOps tools (MLOps or LLMOps, depending on the models in question) to ensure consistent delivery automation.
- By running QA activities in parallel with coding, you can detect potential issues quicker and avoid their costly resolution at later stages. At ScienceSoft, we tailor our approach to QA based on each project's needs while aiming for the same high unit test coverage threshold (95%+) for every investment solution. If you’d like to learn about the QA tactics we apply to inherently complex solutions, such as investment management software, you can visit our dedicated page.
- Well-organized collaboration between the project stakeholders prevents misaligned deliverables, drifting task priorities, and delays. In ScienceSoft’s experience, a formalized approach with clearly defined communication aspects, owners, formats, and schedules always brings substantial value in investment software projects involving large cross-functional teams. For instance, this was the case in our stock investment management software project, which involved 20 data scientists from ScienceSoft alone. In our projects, we typically apply our own set of best practices for efficient and transparent collaboration.
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5.
Integration and data migration
This is the stage where back-end engineers integrate the investment management solution with the necessary corporate and third-party systems. During this stage, you may also need to migrate investment data from your existing system to the new software.
- Most custodian, brokerage, and trading venues offer ready-to-use APIs so that you can quickly integrate your investment management system with their platforms. Integrating with legacy corporate software (e.g., if you plan to retain existing accounting or compliance tools) may require custom connectors. In both scenarios, integration testing is key for ensuring smooth and secure data flows between the connected systems.
- Migrating investment data in small fractions and outside working hours will let you quickly roll back the changes if something goes wrong without affecting investment teams’ workflows. At ScienceSoft, data engineers establish automated migration pipelines with testing checkpoints across data extraction, transformation, and transfer stages. This prevents manual migration errors and helps validate the integrity, accuracy, and completeness of investment data during transition.
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6.
Deployment
Development teams configure investment management software infrastructure, finalize the necessary testing activities, and deploy the solution to production.
- Regulatory mandates evolve rapidly, especially in the field of innovative investment technologies like AI and blockchain. Running a pre-launch compliance audit will let you double-check software compliance with the latest sectoral regulations.
- User acceptance testing (UAT) is critical for making sure that investment teams will have a smooth experience with the new solution. It allows the development team to fix any previously unnoticed usability issues before the software goes live. You can further deploy the solution for a pilot run across a limited number of users to make sure it performs well in live settings. Check how ScienceSoft applied this in a recent bond investment management software project.
- Configuring robust network protection tools (SIEM, IDS/IDP, firewalls, intelligent UEBA, etc.) is another key step you need to complete at this stage. It is necessary for securing your investment management system and its underlying infrastructure against cyber threats.
- Once your investment management software goes live, you need to organize its support and maintenance. Check out ScienceSoft’s dedicated page for ways to establish efficient L1–L4 support operations both for outsourced and in-house scenarios.
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Tech Stack for an Investment Management Platform
Programming languages
Back end
Front end
Front end Javascript frameworks
Mobile
Desktop
Databases / data storages
SQL
NoSQL
Cloud databases, warehouses, and storage
AWS
Azure
Google Cloud Platform
Other
Big data
AI
Machine learning platforms and services
Machine learning frameworks and libraries
Platforms
DevOps
Containerization
Automation
CI/CD tools
Monitoring
Blockchain
Smart contract programming languages
Frameworks and networks
Cloud services
Costs of Investment Management Solutions
Developing custom investment management software may cost from $150,000 to $2,000,000+, depending on the solution’s functional scope, the number and complexity of integrations, as well as performance, scalability, security, and compliance requirements.
Here are ScienceSoft’s sample cost ranges:
$150,000–$400,000
Basic investment management software built on a low-code platform like Microsoft Power Apps. It offers RPA-supported automation and statistical analytics across key investment operations (portfolio management, accounting, reporting) and traditional asset classes (stocks, bonds).
$400,000–$800,000
A custom solution that automates the whole spectrum of investment management operations for a particular domain (e.g., stock, ETF, private equity, or real estate investing). It features intelligent data processing capabilities, rule-based automation for multi-step workflows, and ML-powered predictive analytics.
$800,000–$2,000,000+
A large-scale custom system that handles complex investment management, deal management, and trading operations for both traditional and alternative assets. It offers AI-supported automation, optimization, and analytics features, including innovative capabilities powered by GenAI and LLMs.
Learn the Cost of Your Investment Solution
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Why Build Investment Management Software With ScienceSoft
- Since 2005 in engineering custom solutions for the investment industry.
- Investment IT and compliance consultants with 5–20 years of experience.
- 60+ certified project managers (PMP, PSM I, PSPO I, ICP-APM) with experience in large-scale projects for Fortune 500 companies.
- Principal architects with hands-on experience in designing complex investment solutions and driving secure implementation of advanced techs.
- 550+ software engineers, 50% of whom are seniors or leads.
- Quality-first approach based on an ISO 9001-certified quality management system.
- Robust security management supported by ISO 27001 certification.
ScienceSoft’s Clients Say
Our collaboration was a true partnership. The team was open, attentive to our requirements, and accurate in addressing them. The delivered solution is exactly what we needed.
Sultan Masad
Group IT Manager
The DMS developed by ScienceSoft helped us ensure document integrity and security and accelerate document-related business processes. We are satisfied with the project results.
We were impressed by the smooth communication, attention to our requests, and the team's expertise in web security. We really liked how comprehensive but to-the-point their reports were.
ScienceSoft turned out to be an excellent match. Within just two weeks, their Tier 1 support agents seamlessly integrated into our processes and became an invaluable asset to our team.
We are impressed with ScienceSoft’s pragmatic project management, quality-first mindset, and transparent communication. They are strongly motivated to deliver maximum value with their services.
