Advanced IA and analytics technology: New vision for operational risk management in finance

This is branded content.

Change is the only constant thing in this world. And in finance, Artificial Intelligence (AI) best exemplifies this welcomed shift.

If anything, there is a mound of evidence revealing how AI is transforming the banking industry, especially in risk management, fraud prevention, profitability boost, and, most importantly, value addition.

The idea of what risk management is is being shaped by increasingly meeting client's demands, cut-throat competition, and the quest for continuous improvement.

It is only natural. Anyhow, risk mitigation happens daily. For instance, when choosing to consume healthy meals or work out or, say, choosing not to violate traffic rules, and so much more.

In finance and investment, risk management strategies are enhanced by a merger between AI and a pot of skilled fund managers, bankers, and managers whose overall objective is to extract more from the markets, increasing the portfolio's ROI.

The decision to increase reliance on AI in the past few years, especially, is advised by the increasing operational challenges faced by the industry.

Of note, the Coronavirus pandemic and its mauling of processes had a cascading effect, adversely affecting every facet of the economy, negatively impacting the finance industry.

Unexpected containment measures in early 2020, especially lockdowns, meant supply chain processes were halted.

Subsequently, this meant deep losses, unwinding even the modest gains fund managers' portfolios had managed to register before COVID-19 became the disaster it went on to be.

However, funds that were using AI models managed to salvage and post gains even with the biting pandemic.

Typically, AI models analyse large sets-structured and unstructured-of financial data.

Insights drawn from machine learning algorithm models go a long way in improving the analytical capabilities of fund managers in various financial institutions, including brokerages.

Accordingly, they can pick out risks promptly, allowing them to put up barriers to cushion their bottom lines.

In recent years, governments are aware of the ever-increasing risks and how consequential they can be, even leaving economic shocks.

Since operational risks are primarily due to people's failures or internal policies and systems, creating regulatory clarity can be one way of bringing order in the traditional chaotic regulatory scene.

By formulating and implementing laws that promote the use of AI in finance, industry players can quickly adjust and begin incorporating technology confidently, enhancing their processes.

With guiding laws in place that stipulate the integration of best practices, involving technology to cover the interest of their clients against operational risks, stakeholders in the sphere won't fail to comply lest they be faced with heavy penalties due to their laxity.

Ordinarily, the legacy financial scene was faced with operational risk challenges that came with rising digitisation.

Of note, the boom of Fintech, a term describing financial services that are heavily reliant on technology for delivery, means stakeholders had to expend colossal capital outlays to cover themselves against cyber-attacks.

It was all too common for banks to be inundated by a barrage of denial of service attacks, sophisticated fraud, phishing malware deployed, and so much more ills bombarded against them.

The good news amid these scars is that the sophistication of AI-powered combating techniques has also been rising.

For example, using pattern recognition technology, players can automatically filter out questionable activities and even automate the execution of essential cyber-security tasks-a time-saving move considering the ever-increasing data being uploaded to services across different clouds.

At the moment, fund managers use AI-powered tools to implement various strategies forming a core of their risk management mechanisms.

Top-down, a brokerage could use AI to prevent intrusion while on a lower level, feeding price feeds and other trading metrics into algorithms have drastically improved portfolios.

It only goes on to illustrate just how easy AI can, at one sweep, offer multiple significant benefits in trading, catering for both profit amplification and in fluid, real-time risk management.