By Sunil Sharma, Global Head of Innovation and Digital Solutions
Optimising investments and data management through AI-driven approaches proffers a strategic and multidimensional slant. It adds a cutting-edge precision to financial decision making, estimation of present as well as potential trends, cost-effectiveness, risk management, data quality improvement measures, compliance & regulatory alignment, to name a few aspects.
As per Gartner Inc. (March, 2021) over 75% of venture capital (VC) and initial investor executive reviews will be employing AI and data analytics for all investment decisions by as early as next year (2025). This prediction augured by the research giant stands veritable in today’s investment milieu as some of the most renowned VC corporations and the National Venture Capital Association (NVCA) swear by the exactitude of their AI optimisation tools that yield thrice as precise analysis as any human mind in a significantly more time-efficient manner.
What is AI optimisation?
AI optimisation refers to the use of artificial intelligence (AI) techniques to improve the efficiency, impact, and performance of systems, processes, or solutions. In the context of AI, put simply, optimisation customarily finds the best solution to a problem from an enormous set of possible solutions derived from multifarious combinations and permutations. It identifies patterns and behaviours, surveys factoring variables and arrives at conclusive deductions that further shape predictive decisions.
Apart from the domain of investments and data management, AI optimisation techniques are applied applications across various industries like healthcare, manufacturing, retail & e-commerce, supply chain & logistics, energy & utilities, telecommunications, mass media as well as design tools. In fact, AI Image Optimisation is a graphic tool used not just by creative outfits but also in forensic analysis by laboratories all around the globe. Today, almost every industry is leveraging artificial intelligence in improving executability, efficiency, decision-making, performance and even convalescence during crisis management or damage control.
How is Microsoft cloud AI performance paving the way for finance?
Currently, pioneering technological advancements are being made by using dynamics cloud automation to improve dexterity and dependability in managing Microsoft Dynamics applications deployed on cloud – an infrastruce used by numerous venture captialists, banking corporations and other investment conglomerates. By automating recurring tasks and workflows, it proffers finance corporations with the opportunity to reduce manual effort, minimise errors, and accelerate the delivery of business-critical services and applications.
Microsoft’s cloud AI performance is robust and ever-developing, hence it presents itself as the perfect cluster of instrumental methodologies that only propel corporations towards a more data-driven version of themselves.
There are several key strategies that can be employed to ameliorate both efficiency and efficacy through
Microsoft cloud optimisation when it comes to investments and data management:
1. Predictive analytics for investment decisions: AI optimisation tools can analyse historical data, market trends, and other relevant factors to predict future investment outcomes. Machine learning algorithms can identify patterns and correlations that humans might overlook, aiding in making informed investment decisions.
2. Portfolio optimisation: AI algorithms can optimise investment portfolios by balancing risk and return. Through techniques like Markowitz’s Modern Portfolio Theory or more advanced methods such as genetic algorithms or reinforcement learning, AI can suggest optimal asset allocations based on an investor's risk tolerance and investment goals.
3. Sentiment analysis and news monitoring: Natural Language Processing (NLP) is one of the intuitive
AI optimisation techniques that can analyse news articles, social media posts, and other textual data to gauge market sentiment and identify potential investment opportunities or risks. This can help investors stay ahead of market trends and sentiment shifts.
4. Algorithmic trading: AI-driven algorithms can execute trades automatically based on predefined rules and parameters. High-frequency trading (HFT) algorithms, for example, can analyse market data and execute trades within milliseconds to capitalise on fleeting market inefficiencies.
5. Risk management and fraud detection: AI can help in identifying and mitigating various types of risks associated with investments, such as market risk, credit risk, and operational risk. Additionally, AI- powered systems can detect anomalies and patterns indicative of fraudulent activities, enhancing the security of investment processes.
6. Personalised investment recommendations: AI can analyse individual investor preferences, risk tolerance, financial goals, and past investment behavior to provide personalised investment recommendations. This can improve customer engagement and satisfaction while guiding investors towards suitable investment opportunities.
7. Data quality management: AI can assist in ensuring the quality, accuracy, and integrity of investment-related data. Machine learning models can detect and correct errors, inconsistencies, or missing values in large datasets, thereby enhancing the reliability of investment analyses and decisions.
8. Cybersecurity: Given the sensitivity of financial data, AI can play a crucial role in detecting and preventing cybersecurity threats. Machine learning algorithms can analyse network traffic patterns, user behaviors, and other data sources to identify potential security breaches or malicious activities, safeguarding investment-related information. A large number of financial corporations use Microsoft cloud as a reservoir of crucial big data and information that requires impenetrable security against breach, which is why, automation in Microsoft cloud helps create a fortress that not only adds secure layers around the data but also manages it with accuracy.
Conclusion and key takeaway
Perhaps the most paramount corollary of deploying all of the above strategies and AI optimisation tools is the assurance of continuous learning and improvement. The reason being, AI systems can perpetually learn from new data and feedback, bettering their accuracy and effectiveness over time. By incorporating real-time market data and performance feedback, AI-driven investment strategies can adapt to changing market conditions and investor preferences, thereby, proving to be a highly adaptative instrument that can scrutinise, examine, predict, advise and dynamically change as per the need of the hour.