How AI Plays Into the Future of Risk Management

How AI Plays Into the Future of Risk Management
Risk analysts have always relied on data to guide decisions toward strong growth potential and away from high-risk strategies. This used to be a fairly linear process, but now that up to 90% of our data is unstructured, information is not only difficult to organize into digestible formats but also produced in volumes that go beyond the capabilities of human analysts aided by conventional data systems.

Artificial intelligence and predictive analytics hold the promise of tackling the data burden and keeping risk predictions agile to external trends — but they need to be applied strategically to present real value and minimize business risk. 

The Risk of Siloed Approaches

Historically, risk analysts have been able to make sense of complex yet structured data. Although nothing in these methodologies are broken per se, the way we use data is transforming irreversibly. 

For a business discipline that should offer exactly the opposite, it’s clear that the standard practice needs to be adjusted to keep predictive analytics accurate. With the majority of all business services and consumer activity now taking place digitally, data is produced in vast, unprecedented volumes that are virtually impossible to neatly organize into structured, linear data sets for interrogation. 

This is especially true for real-time data, such as payment transactions that take place minute-to-minute, or conversational data taking place across social or customer service platforms. This creates a difficult conundrum as — without both real-time and historical data insights in the hands of analysts — a vital piece of the puzzle is missing.

But it’s not just the nature of unstructured data itself that demands this change; the shockwave effects of the COVID-19 pandemic put the need for resilient risk management into sharp perspective. 

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The Snowball Effect

While consumer trends and economic uncertainty may have taken years to develop in the past, microtrends with the power to snowball into disruptive forces can now take place in a matter of months.

With cognitive analytics, unstructured data can not only be processed but analyzed in real-time using sub-specialisms like Natural Language Processing (NPL). 

With powerful predictive analytics to hand, boardrooms stand a better chance of staying abreast of these hurdles and adjusting business models accordingly. Are consumers starting to boycott a certain manufacturer? Is a trading route no longer reliable due to political instability? Are clients indicating their desire to see cryptocurrency payment options with their service providers? 

The answers to these types of questions can help companies minimize risk, take rapid action on the future of the business and bank on decisions that build towards stability. 

Source: Brink News

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