Artificial Intelligence (AI) and Machine Learning (ML) are exploding in financial services.
An Economist Intelligence Unit (EIU) research report found that 86% of Financial Services executives plan on increasing their ML and AI investments through 2025.
Why? Because these technologies have greater untapped potential than any other to deliver high-value outcomes in the sector.
A combination of accelerated public cloud adoption, modern software engineering practices and increasing availability of high quality, freely accessible data are beginning to unlock new opportunities for financial services organisations to apply ML and AI to complex business opportunities.
They promise a number of otherwise-impossible outcomes:
- Make markets more efficient, accessible and transparent
- Discover customer needs and tailor products to those needs without adding undue risk and overheads on financial services providers.
At the same time, however, increased adoption is piquing the interest of the regulators.
In 2019, for example, the Bank of England (BofE) published a report studying the patterns of adoption of machine learning across financial services. In 2020, a public-private working group was established by the BofE to help the sector as a whole better understand the impact of AI and ML on financial services. In 2021, the US Office of the Comptroller (OCC) published a request for information on financial institutions' use of AI and ML.
This is a global phenomenon, also: in 2021, the Monetary Authority of Singapore (MAS) published a set of principles that are aimed at promoting fairness, ethics, accountability and transparency in the use of artificial intelligence across the Singapore financial sector.
This is because risks do exist: if optimal controls, governance and break-glass procedures are not in place, poorly-configured models could increase risks or create market volatility.
But different kinds of financial services organisations have different opportunities and risks for applying ML and AI.
Whether you are a retail bank, an investment bank or an insurance organisation will determine much of your AI/ML program: your degree of access to high-quality data, the types of techniques required to make model-based decisions, the need to reimagine business processes and systems of engagement to support customer acquisition and retention and so on.
Now, the financial services industry is complex, so the potential applications for AI and ML are literally infinite! But here I wanted to highlight the main categories of use cases across these three broad sectors.
Retail Banking:
- Fraud Detection
- Vulnerable Customer Identification
- Customer Engagement
- Customer Sentiment
- Intelligent Document Ingestion
Investment Banking:
- Algorithmic Trading & Quantitative Risk Management
- Anti Money Laundering (AML)
Personal Insurance & Commercial Insurance:
- Dynamic Pricing
- Claims Management
- Fraud Detection
- Lapse Management
- Recommendation Engines & Product Cross-Selling
- Automated Underwriting
We should stress that many of these use cases are interchangeable across financial services. For example, AML controls are required across both the retail and investment banking domains. Whilst the need to increase the detection of fraudulent transactions across both retail banking and the insurance sector is imperative.
In future blogs, we will go into great detail on each of these use cases, detailing the key challenges and how AI/ML can contribute.
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