FinTech Innovations for Investment Management Funds (part 1)
Key points
- WHAT is FinTech and WHY it’s so crucial on the buy-side right NOW?
- WHAT is the Buy-Side and WHAT kind of FinTech Solutions the need?
- WHAT does FinTech has to offer for Investment Management Funds?
- WHAT are the examples of FinTech companies and their products that we recommend for Investment Funds?
- WHERE to go next for more information?
What are the examples of the buy-side?
Buy-side analysts will determine how promising an investment seems and how well it coincides with the fund’s investment strategy.
They’ll base their recommendations on this evidence. These recommendations, made exclusively for the benefit of the fund that pays for them, are not available to anyone outside the fund.
What type of technology solutions investment management fund needs?
- Research and Idea Generation
- Market Insights
- Company & Industry Analysis
- Quantitative Analytics
- Research Management
- Compliance and Risk Oversight
- Investment & Trade Compliance
- Transaction Cost Analysis
- Surveillance & Trade Reconstruction
- Regulatory & Risk Reporting
- Portfolio and Risk Management Tools
- Portfolio Analytics
- Portfolio Construction
- Performance Measurement & Attribution
- Risk Analytics
- Benchmark Indices
- Post trade and Operations
- Reconciliation & Exception Management
- Allocation Matching & Trade Settlement
- Collateral Management
- Corporate Action Processing
- Order and Execution Management
- Liquidity Analysis & Price Discovery
- Order & Allocation Management
- Execution Management
Factors for and Against Adoption of buy-Side OMS
Adoption promoters
- Pressure to reduce investment management costs
- Push to consolidate vendor relationships
- Data and reporting requirements
- Need to consolidate disparate systems to reduce operational risk and achieve better scalability
Adoption inhibitors
- Saturation and maturity of the marketplace
- Total cost of replacement
- Continued pressure on buy-side for better cost control
The proliferation of FinTech innovations in Investment Management
- AI & ML
- Big Data
- Blockchain & DLT
- Robo-Advisers
- RegTech
- Cloud/API
- WealthTech
Some use-cases of how FinTech tools can help Investment Managers
Analysis of large data sets with Big Data
- Security prices, financial statements, economic indicators and qualitative bits of info leading to a better investment decision making process
Automated trading with Cloud/API
- Systems built to identify systematic investment strategies and automatically execute multiple trades over seral financial markets worldwide
Data science analytical tools
- ML algorithms built to sort an enormous amount of financial data leading to identifying trends and investment opportunities
Automated advice with Robo-Advisers
- Internet based intelligence models that provide investment advise with minimal human intervention
Improved Risk management
- Big Data models built to aggregate, analyze and interpret data in real life in order – to identify weakening positions & adverse trends in advance
Examples of the areas where FinTech adoption is necessary according to Investment Association
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AI assisting asset allocation, security selection, portfolio construction and trading
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Operational modernization & RPA Gains within and between middle and back office
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AI and data analytics Fund marketing and portfolio decision- making and risk analysis
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Digital platforms with DLT and API architecture Allows Integration of 3rd party platforms for best of breed experience
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Changing pattern of distribution Role of Investment Manager and Advisor/Distributor
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Cloud migration Allows re-optimization of internal architecture and evaluation of vendor security
Factors for and Against Adoption of buy-Side OMS
- Current Operation models don’t work anymore (too manual and no alpha generating)
- DLT will deploy T+0 settlement cycle (no more failed trades and trade mismatches)
60-70% of staff on the buy-side works within Operations, accounting, IT
- Reduced buy-side participants fees and commission
- Reduced costs for broker dealers and custodians
26% Artificial Intelligence
Artificial Intelligence 36% DLT and Blockchain
Data science analytical tools
16% Regulatory Technology
6% Quantum Computing
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