3 ways AI optimisation can revolutionise the financial sector

Financial services are increasingly implementing AI technologies in order to help analyse massive volumes of data, identify market trends and prioritise tasks. On top of this, it is being used to identify fraud, personalise the customer journey, as well as cyber security and general risk management.

by Dr Leslie Kanthan, CEO and Co-Founder, TurinTech

The volume of information and data generated by financial institutions is huge, and AI is proving to be a pivotal cog in the sector machine by handling this data more efficiently. 

According to a past Accenture report, banks could increase revenue by 34% by this year if they invest more readily in artificial intelligence. Fortunately, this report also concluded that in general the banking industry and its executives and employees were optimistic and positive about the impact AI could have on their organisation. 

It comes as no surprise that as of February 2022, 56% of financial firms have implemented AI in business domains like risk management and 52% in revenue generation areas.

So where are we today? Is AI just another buzzword or can it really help deliver efficiency and increase productivity in the financial sector? Let’s dive into three important ways AI optimisation can revolutionise the financial sector.

Empowering innovation at speed and scale

Operationalising AI at scale is still a big issue for many companies, with IDC citing only 25% of firms running an AI project having developed an “enterprise-wide” AI strategy and many of these projects are doomed to fail. 

Operating in a highly regulated industry, financial institutions often have to trade-off between model performance and explainability. But this is where AI optimisation can help, which uses AI to optimise model and code, enabling full transparency and explainability, without compromising on the accuracy and running speed of the model.

Unlike other AI automation tools, AI optimisation platforms can help financial firms build custom models with multiple criteria, at scale. What this means for financial services is that these tools can create better and faster algorithms for their unique business problems, optimising business processes efficiently and effectively.

Elevate ESG compliance with greener AI

A Global Data survey reported how the pandemic has pushed ESG executives to increase their focus and action on ESG issues. 

As an industry, it’s time to reconsider our carbon footprint and start to prioritise sustainable change. WWF and Greenpeace report that UK Financial Institutions were responsible for 805 million tonnes of CO2 emissions, almost 1.8 times the UK’s domestically produced emissions.

AI optimisation will be a force for good in meeting sustainability goals, with machine learning models becoming faster, more efficient, and consuming less energy. Green AI ultimately integrates technology and sustainability into a unified ecosystem. 

With more change and uncertainty to come in the year ahead, AI optimisation will be there to support and transform those businesses that are willing to rethink existing processes and agendas. Ultimately every organisation has a responsibility to be contributing positively to the climate crisis, and optimising processes is certainly a step in the right direction.

Accelerate algorithmic trading speed and improve accuracy

According to Coalition Greenwich’s report, 28% of FX executives said they are currently using execution algos, with 51% confirming they intend to increase their use of algos. 

If and when applied correctly, AI can bring impactful benefits to algo trading. Take, for example, a case when a hedge fund’s statistical models are underperforming, unable to take advantage of more complicated patterns in ever-increasing data types and volumes (e.g. Market price and volume data, third party data, proprietary data).

What can the trading team do? 

By leveraging AI optimisation platforms to accelerate the end-to-end trading strategy development process,  they can create dozens of optimal models in days for different prediction needs, such as the price, price percentage change, up/down momentum, on large amounts of data. The fund can then automatically identify the most effective signals among thousands of data features, avoiding spending hours to do so manually. Applying this to the real world can make trading strategy development 25 times faster and increase the annual return rate by 90%. 

The bottom line

Through the use of AI technology, the financial sector is able to significantly improve its performance and revenue in more ways than one. McKinsey estimates that AI could generate up to $1 trillion additional value annually for the banking industry globally.

Furthermore, in today’s constantly evolving landscape, staying innovative and agile is crucial. Having technology that not only empowers this change and innovation at scale but compliments it with ESG  considerations will be of huge importance to the sector moving forward. Vital innovation is required to be implemented at speed and scale in order to keep up with competitors, which can be achieved through the implementation of AI.