The Terra (Luna) crash in the first half of 2022 and the bankruptcy of Three Arrows Capital in July have resulted in a shift in attention from investors, as sensed by KPMG’s Pulse of Fintech, and they now prefer more secure and regulated avenues. The volatility of confidence in financial software development is more evident and disruptive in domains like investment banking than others. Emphasizing the importance of stability and reliability, investors seek digital means to be cognizant of their anxieties. Thus, democratization is a rocky road particularly for investment banking landscape.
In such times, fintech advancements in technologies like Artificial intelligence and Robotic Process Automation (RPA), is meeting with somewhat less hostility than the cryptos and NFTs of the financial world. Today we have AI-powered RPA software automation services for Banking, Core operations, credit management, and even risk and compliance management. Therefore, in this blog we will explore how the two can do wonders for investment banking.
By incorporating AI capabilities, RPA can uplift platform engineering services, and fintech app development for intelligent automation. Modernization services, on the other hand, can even empower the existing fintech application development to integrate robotic process automation in finance. Here’s what AI/ML services and RPA can bring for Investment banking in particular:
AI has significant potential to transform investment banking in several ways:
- Investment Advisory: The most important change that AI/ML+RPA can bring is managing vast amount of assets and providing automated investment advice to users. This is where the core essence of democratizing investment lies. While those who could previously hire portfolio management experts can now access these fintech platforms and apps that can help them analyze market trends, assess risks, and choose amongst personalized investment recommendations..
- Informed Decision Making: Fintech companies are also leveraging AI to offer more advanced investment management solutions for hedge funds, commodity trading, and asset management. Combined with RPA, these AI algorithms can help with the analysis of vast amounts of data, identification of investment patterns, and therefore informed investment decisions.
- Advanced Data Analysis: AI algorithms can analyze large volumes of financial and market data, extracting valuable insights for investment decision-making. By integrating AI with RPA, investment banks can automate data gathering, cleansing, and analysis processes, enabling faster and more accurate data-driven investment strategies. This integration can also support portfolio management by continuously monitoring market trends, assessing risks, and suggesting optimized investment allocations.
- Enhanced Risk Management: AI integrated with RPA can bolster risk management practices in investment banking. AI algorithms can analyze vast amounts of data in real-time, identify anomalies, and detect potential fraudulent activities. By automating risk assessment processes, investment banks can achieve more accurate and timely risk identification and mitigation, ensuring compliance with regulations and protecting against financial crimes.
- Regulatory Compliance: The fintech application development can also integrate better QA services for compliance management thanks to integration of AI and RPA. The two together can fintech solutions with algorithms that can automatically check the performance for regulatory compliance while also tracking any changing or evolving regulations. RPA can also automate compliance-related processes, such as data reporting and audit trail generation, reducing human error and enhancing compliance efficiency.
- Improved Customer Experience: AI-powered chatbots and virtual assistants can provide personalized and interactive customer experiences in investment banking. When integrated with RPA, these AI-driven virtual agents can handle customer queries, perform basic account transactions, and provide relevant investment advice. This combination of AI and RPA creates efficient and responsive customer service, reducing response times and enhancing overall satisfaction.
However, the skepticism against digital advancements in investment banking is not misplaced. While venturing into the deep woods of investment, fintech companies should be aware of some AI and RPA uncertainties that could put their efforts in jeopardy. Here are some challenges that the fintech companies need to be cognizant of while building solutions for investment banking:
- Data security: For any kind of financial software development, information is highly sensitive, requiring robust security measures to protect it. Scaling such security measures for investment management might have its risk as the operations here deal with a significant amount of data. Therefore, AI and RPA based solutions should be secure by design, processes and databases. It is necessary that the data operations are optimized for their performance to maintain a smooth user experience.
- Change Management: Another challenge in implementing AI and RPA solutions is more cultural than operational. The investment banking workforce might have to be reskilled for changes in roles and responsibilities. Investment banks need to manage the transition effectively, providing adequate training and support to employees and ensuring a smooth adoption of AI and RPA technologies.
- External Attacks: Investment banking deals with sensitive financial data, making security a critical concern. Implementing AI and RPA requires robust security measures to protect data privacy, prevent unauthorized access, and mitigate the risk of cyber threats or data breaches.
- System Integration and Legacy Infrastructure: Investment banks often have legacy systems and complex IT infrastructure. Integrating AI and RPA technologies with existing systems can be challenging, requiring careful planning and coordination to ensure seamless integration and minimize disruption to ongoing operations.
Fintech application development is witnessing the emergence of AI in almost all of its use cases including AI in banking, AI in financial software development and even AI in wealth management. The convergence of AI, RPA can further contribute to the democratization of investment as well. Test automation services, and devsecops consulting services for fintech can be leveraged to build smart AI+RPA based automation for investment banking. If you too wish to navigate the challenges of implementing AI and RPA in investment solutions or any fintech solutions for that matter, do talk to our fintech experts today.
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