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Top 6 Trends in Artificial Intelligence (AI) Risk Management

Risk is a natural part of running a company. However, only 8% of businesses are adopting artificial intelligence (AI) diligently, separating these companies from the rest of the pack. The businesses that flourish practice proper risk management. Machine learning is a subset of AI that employs data analysis, allowing companies to predict these risk levels. As AI advances rapidly, trends emerge that show just how useful data-driven decisions may be. 

 

1. Increased AI Use

As with most instances of innovation adoption, more people will adopt AI solutions as time goes on. This tech can be used for: 

  • Risk-based decision making; 
  • Follow-up lead nudging; 
  • Chatbots for handling customer issues; 
  • Revenue forecasting; 
  • Targeted marketing; 
  • Natural-language processing;
  • Recruitment automation. 

This is just the tip of the iceberg, and most of these technologies and processes are already in use. There are undoubtedly more uses of AI to come, as well as a surge in the number of businesses that will adopt existing practices. With early adopters to test out the waters, AI can work out the kinks and become more accessible to a wider range of businesses. 

 

2. Integrated Risk Management 

Unless your company completely outsources every single process, you are responsible for monitoring risks. As such, integrated risk strategies are essential to the success of an organization. AI and machine learning can optimize integrated risk strategies at every stage of the process, including: 

  • Planning — using risk analytics software to figure out the acceptable thresholds of risk level for each situation and each party involved; 
  • Identification — obtaining automatic predictive analytics reports from these risk management tools, outlining all possible risk factors and their possible causes and effects; 
  • Assessment and analysis —  using these tools to estimate the probability of certain outcomes and the severity of those outcomes;
  • Response creation — developing a response to the threat or negative outcome based on predictive analytics (particularly those within a claims management system) that is shown by historical data to have worked well financially in the past;  
  • Monitoring — utilizing risk management AI to keep tabs on suspicious activity and patterns indicative of previous breaches and issues; 
  • Review — analyzing performance of integrated risk management according to specific key performance indicators (KPIs) that are automatically tracked by AI. 

Businesses benefit from having this automated analysis at every step of an integrated risk management strategy. Without it, it’s merely a guessing game that you can’t afford to lose. 

 

3. AI and Cloud Computing 

Previously, mass amounts of storage were needed to keep a company’s data and run the AI analysis. This — along with the processing power needed to run advanced analytics — proves costly, deterring some businesses from investing in data analysis in the first place. However, the benefits of using a cloud-based, AI-as-a-service (AIaaS) are clear:

  • Less costly;
  • More secure;
  • Collaborative; 
  • Agile and able to adapt to fast-moving tech advancements;
  • Easily scalable; 
  • Trained models; 
  • Less susceptible to bugs and breaks;
  • Provides more access to AI interfaces; 
  • Enhances machine learning for continued innovation.

AIaaS provides a low-risk environment in which companies of all sizes can test out established models and AI-powered functions that weren’t possible before. For instance, car companies have started refining smart recognition of objects on the road. To keep up, other automotive businesses may put forth a smaller investment in AIaaS, allowing for less risk and more profits in the long run. 

 

4. AI Ethics

Using AI to analyze data may appear as though it removes all bias on the surface. However, humans create AI algorithms and solutions, which means the algorithms are inherently exposed to human biases. Whether intentional or not, training machines to analyze data may expose these algorithms to certain preferences. 

Furthermore, past data used to predict future movements in the company may perpetuate skewed results. An example of this is evident in recruitment software. Amazon, for instance, had to tweak its hiring algorithm due to certain filtered words excluding female applicants more often than their male counterparts. There are other instances of biased algorithms that perpetuate discrimination:

  • Facial-recognition tech failing to recognize darker complexions; 
  • High-interest credit card ads targeting African Americans;
  • The algorithm used by judges to determine detainment or bail release pending trial assigning African Americans a higher-risk score based on historical demographic information; 
  • Amazon’s algorithm excluding poorer neighborhoods from same-day shipping.

The good news is that machine-learning specialists can pinpoint these problems and offer solutions. Of course, the best way to avoid these biases is to choose software that has been meticulously designed, tested, and empirically reviewed. 

 

5. Disruptive Workplace Technology

Often, it’s difficult to change tech in a work environment. Even if you get budget approval, it may be harder still to encourage employees to adopt the new solutions. Only 10% of SMBs have successfully integrated AI into their workflow as of 2019. 

However, AI is still expected to massively disrupt the workforce as a whole. While it may not look how many people originally envisioned it — think: robots ruling the world — there is still the strong possibility of tasks being overtaken by automation. From HR to assembly lines, machine learning is most likely going to speed up many processes across many different industries — including risk management.

 

6. Consolidated Analytics 

Occasionally, data analysts find themselves drowning in too much data and not knowing what to do with it. When these data lakes form, it means there’s too much information to analyze and glean insight from — as such, this information generally sits on servers, takes up storage, and eats into company overhead. Fortunately, even though it’s not humanly possible to analyze these data lakes, it’s possible for AI.

Machine learning takes unsystematic data and turns it into actionable intelligence. Simply put, you can now use AI to synthesize your data and return automated predictive analytics reports. This way, instead of paying for data on servers that you can’t even utilize, you’re paying for analyses that could benefit your company for years to come.

 

Next steps

If you would like to learn more about Ventiv's Analytics offering please contact VINAY KARLE.

Jul 23, 2021

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