What can AI do for financial services?

Chances are, you can't scroll through today's online media without encountering the term "AI.".

The journey to this phenomenon has been nothing short of remarkable. In just under seven decades since the coining of the term "Artificial Intelligence," AI has seamlessly integrated itself into even the most dynamic and high-paced industries.

Forward thinking C suite executive managers and business owners today, are now actively exploring new AI use in finance and other areas in an attempt to get a competitive edge on the market.

Almost each passing day ends with news of a company making an investment into AI application.

SoftBank's founder and chief executive, Masayoshi Son, is looking to invest tens of billions of dollars in AI

AI in Finance: What works

AI in finance is used broadly and cuts across multiple sectors of the finance industry, but in comparison to other sectors, the financial services industry is vastly behind other consumer-centric industries in a lot of ways.

Currently some examples of how it is used now includes:

In making Credit Decisions

One of the most prominent applications of AI in finance is in credit decision-making. Traditional credit scoring models are being augmented and, in some cases, replaced by AI-driven systems. These systems can analyse a vast array of data points, including unconventional ones like social media activity and online behaviour, to assess creditworthiness more accurately. As a result, lending institutions can make better-informed decisions, expanding access to credit for a broader range of borrowers.

In Risk management

AI can help prevent financial disasters, thanks to current abilities to make appropriate, informed decisions about risk and capital allocation.

Risk management is at the center of the financial sector.

AI plays a pivotal role in identifying, assessing, and mitigating risks. Machine learning algorithms can analyse historical data and detect patterns that humans might miss. This enables institutions to manage risks in real time, respond swiftly to market fluctuations, and enhance portfolio optimisation.

In fraud prevention

AI has also been very useful in fraud prevention, it has opened up a new world of opportunity to tackle and reduce fraudulent activities such as money laundering.

AI algorithms continuously monitor transactions, looking for unusual patterns that may indicate fraudulent activity. These systems can rapidly detect and respond to threats, reducing financial losses and enhancing customer trust.

In process automation

Efficiency is at the core of AI's impact on finance. Automation of routine tasks, such as data entry and reconciliation, frees up human resources to focus on more strategic activities. This not only reduces errors but also accelerates decision-making processes.

What is coming next?

The future of AI in finance holds exciting possibilities:

Explainable AI (XAI)

As AI systems become more complex, there's a growing need for transparency and interpretability. XAI aims to make AI decisions more understandable and accountable.

Advanced Personalisation

AI will continue to refine customer experiences by offering personalised financial services, from investment advice to tailored insurance packages.

Blockchain Integration

AI and blockchain are converging to create more efficient, transparent, and secure financial systems. Smart contracts and decentralised finance (DeFi) are emerging trends.

AI-Driven Regulation and Compliance

AI will play a vital role in automating regulatory compliance, helping financial institutions stay on the right side of the law.

Quantum Computing

While still in the experimental phase, quantum computing has the potential to advance financial modeling, risk assessment, and cryptography.

Cybersecurity: AI will continue to evolve in the battle against cyber threats, utilising predictive analytics to proactively defend against attacks.

The rise of AI in financial services is raising expectations from organisation. Across the financial landscape, providers are undergoing a profound reevaluation of their technology investments. This rethinking encompasses all aspects of their tech infrastructure, marking a pivotal shift in their approach.

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