Banking and finance sector is increasingly implementing AI and automation with increasing need for reduced cost and ease of operations in areas such as investment banking.
Investment banking came into existence to fulfil the long-term capital needs of the businesses. Currently it is witnessing technological transformation owing to the emerging digital transformation which includes technologies such as artificial intelligence (AI), machine learning (ML) and Big Data analytics. Investment banks are using predictive analytic tools to analyse the market related data to come out with the best suitable investments and pitch for deals and transactions. This tool is saving time and increasing efficiency of the bankers to provide better insights.
There are several complex algorithms that are capable of an understanding trader’s need across equities as well as assets including currencies, credit and commodities. For instance, Goldman Sach’s Marcus is a consumer lending platform that requires limited or no human interference. These automations will exponentially increase the number of deals for financial institutions due to significant reduction in deal execution cost.
Digitalization is generating huge volumes of machine data. However, limited datasets are used for decision-making in investment banks owing to complex processing of unstructured data which includes news articles and presentations related to market and others. Therefore, there is need for technology in BFSI, that can help in building prediction models for mergers & acquisitions, trading, enhance due diligence processes and automation of the workflow.
On the other hand, A major challenge is that BFSI sector is generally wary of the data being held with third party vendors or implementation partners for digital technologies. Hence, they are slow in adopting technologies and cautious about any data leakage in the process.
Cognitive technologies including artificial intelligence are capable of processing fragmented and unstructured datasets to provide actionable insights. Some of the advantages of implementing AI in investment banks are as follows:
- It will fortify the deals and the transactions in the areas such as Merger and Acquisition
- Huge volumes of data will be analysed, and actionable insights will help portfolio managers to be proactive with clients
Conclusive, adoption of AI in investment banking will open new business opportunities for financial institutions in the areas including M&A, trading (includes hedge funds, equity), credit and others. Capability to analyse huge volumes of unstructured datasets will generate insights which will allow investment bankers to take informed decisions, resulting in enhanced customer experience. Furthermore, AI will also help in detecting anomalies and will help in risk mitigations associated with financial transactions.
ICT – Research Analyst