Hyperinflation, interest rate rises and the cost of living crisis are just some of factors piling increasing pressure onto the financial services sector.
This has been exacerbated by the recent demise and acquisitions of several banks, sending shockwaves across markets that haven’t been seen since the Global Financial Crisis of 2008.
Firms need to take swift action to return the financial system to safer harbours – and following the tried and tested traditional approach is no longer going to be enough.
Emerging technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are presenting new ways to address these critical concerns, as well as opportunities to optimise operations and strengthen balance sheets.
Take a look at 10 areas where AI can be used to fight back against the colossal problems faced by financial institutions – as well as some of the massive opportunities it unlocks…
Utilising AI in this way can help organisations to successfully navigate challenges such as inflation, interest rate fluctuations, and geopolitical tensions.
ML (such as large language type models) allow institutions to optimise their balance sheets by identifying opportunities for growth, managing risks, and enhancing capital efficiency.
Enriched analytics also help to achieve operational effectiveness by optimising processes, improving decision-making, and reducing costs.
Firms are trading millions if not billions of assets on a daily basis across multiple regions, markets and products, all the while having to keep in line with tough regulatory compliance.
Calculating the potential return on equity and being able to project the risk is therefore imperative to secure new business, retain customer confidence and remain ahead of the competition.
ML allows firms to closely monitor market conditions and understand events that affect stock prices by analysing multiple internal and 3rd party data sources in parallel. This also means increased accuracy and reduced chances of fat finger mistakes.
AI and data analytics can significantly improve the risk management controls which are essential to minimise losses and protect the financial system.
In addition, AI-driven risk models can analyse vast amounts of data to identify potential threats and vulnerabilities.
This means organisations can implement appropriate controls, monitor their effectiveness, and adapt strategies to mitigate potential losses.
Operational resilience is crucial for financial institutions and is particularly important at a time when regulators are applying pressure for firms to conform with increasingly stringent regulations.
AI and data analytics can help financial institutions to anticipate and adapt to disruptions more effectively.
This includes scenario analysis and stress testing, which can help identify potential vulnerabilities and develop contingency plans to minimise the impact of unexpected events.
Advanced analytics can be used to gain a better understanding of an organisation’s Environment Social & Governance (ESG) risks and opportunities.
ESG factors are becoming increasingly important to investors and stakeholders, so financial institutions must integrate these considerations into their decision-making processes.
These insights can also be used to demonstrate an increased awareness of ESG when selling to customers - younger investors in particular can seemingly be swayed by ethical data driven investment products.
Financial institutions are subject to stringent regulatory reporting requirements designed to promote transparency and protect consumers.
In the United Kingdom, the FCA recently stated that they “remain concerned that many payments firms do not have sufficiently robust controls and that as a result some firms present an unacceptable risk of harm to their customers and to financial system integrity."
One option firms have is to throw more people at the problem, however this isn’t scalable and as such, firms need to explore AI and ML approaches to tackle these commitments.
In their 2020 report, UK Finance conservatively forecasted that unauthorised financial fraud losses totalled £824.8 million. Fast forward to the first half of 2021 and a revised report indicated that there had been a 30% increase in year-on-year fraud cases.
There is massive value in collecting much more detailed and comprehensive data to enrich the 360 view of fraud detection.
Machine learning allows vast volumes of streamed/continuously processed data to be analysed in near real time to detect and interject in scams. This means firms can perform pattern detection on known tactics and behaviours of scammers and protect genuine customers from harm.
The use of AI and ML in financial services is transforming the way institutions engage with customers and tailor their offerings.
A 360° view of the customer involves consolidating data from various touchpoints, including transaction history, demographics, online behaviour, and interactions with customer support.
AI and ML algorithms then analyse this data to uncover hidden patterns and preferences that may have gone unnoticed through traditional analysis methods.
This leads to improved personalisation, targeted marketing, and superior customer experiences.
The advent of digital and mobile banking has eroded the presence of high-street banks and many firms have deployed chatbots to expedite customer queries for everyday banking tasks.
By building vast data sets, organisations can train ML & AI driven live chat interactions to support queries such as requesting new debit cards or updating addresses through both mobile and web channels.
These AI-powered chatbots not only enhance the customer experience, but also reduce the workload on customer service teams, allowing them to focus on more complex issues.
AL and ML can detect early signs of dissatisfaction and proactively address any issues, which is particularly valuable at a time when firms across the banking sector have experienced sizable asset withdrawals.
This data can also be utilised for targeted marketing campaigns, ensuring that customers receive relevant and timely promotional messages based on their assets, investment portfolio or historical product acquisitions. This not only increases the effectiveness of marketing efforts but also enhances the customer experience by reducing the likelihood of receiving irrelevant or unwanted communications.
However, introducing AI and ML powered solutions is a sophisticated undertaking and to capitalise on these rapidly evolving capabilities organisations must rethink how existing procedures are executed. They need to be prepared to acquire new skills, establish modern ways of working with data and to put in place new technology foundations that break down years of siloed business units.
Mesh-AI has a proven track record in reimagining how financial services enterprises operate, making data and AI their competitive advantage. If you're seeking to transform your enterprise into a data-driven and AI-enabled organisation, get in touch with us at firstname.lastname@example.org
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