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6 Ways To Use Machine Learning for Data-Driven, Risk-Based Decision Making in Your Organization

Calculating business decisions using algorithmic risk identification comes not without its risks. As data becomes increasingly available, there are almost infinite possibilities when determining risk levels. This may quickly become overwhelming — resulting in “data analysis paralysis”. However, there are ways to make data work for you. 

Any potentially risky decision in a business should be made with data-driven considerations in mind. With the rise of artificial intelligence (AI) and machine learning, there are more ways to obtain data than ever before. Machine learning is a subset of AI that involves machines processing data associated with a task, analyzing that data, and then using that analysis to alter and optimize how the original task is performed — all without human intervention. This allows humans to utilize AI to inform decision-making in different areas of any size business, resulting in better or preferred outcomes. 

 

Understanding Risk Management

Any move a company makes has the potential to be risky. If a wrong decision is made, impacts are most prominently seen as a loss of revenue. There are five types of risks in business:

  • Strategic — the initial business plan and any modifications in strategy made during the lifecycle of a company;
  • Financial — the investments and business ventures that result in loans, revenue loss or gain, bankruptcy, or other monetary situations;
  • Operational — the internal processes by which a company operates;
  • Compliance — the adherence to rules and regulations that determine the legal standing of a company;
  • Reputational — the public perception of a company and any threats to their good name. 

All of the above business risks need to be managed or they may result in loss of revenue or even business closure. Thankfully, integrated risk management software exists to help you calculate risks and predict outcomes. This consolidates all of your company’s data, allowing you to identify, prioritize, and monitor risks in the following ways.

 

1. Strategic Planning 

All businesses begin with a strategy in some form. If this strategy isn’t updated along with the company’s growth, flaws may start to creep in and impact the business negatively. This isn’t immediately obvious, and it may take some trial and error to figure out where the strategy needs to be revised. 

Some examples of how to facilitate strategic growth using data-driven risk management include: 

  • Making calculated pivots based on projected supply and demand;
  • Detecting fraud, investigating the source, and avoiding future mishaps; 
  • Upgrading technology based on the business landscape;
  • Strategic merging with another company based on past data and predicted industry trends.

It’s natural to tweak your business strategy throughout the years. Trends and goals will ebb and flow, but there will be identifiable patterns the longer you are in the game. Using machine learning simply informs your next steps with data-driven reasoning. Rather than taking a leap of faith, AI risk management platforms can show you the best course of action based on actual patterns in data.

 

2. Financial Forecasting

Finance is the sector of business that makes the most obvious impact. Machine learning can take the mass amounts of financial data your business inputs and consolidate them into something meaningful and actionable. Here are some of the best uses for machine learning to mitigate financial risks: 

Of course, financial planning will look different in every organization. However, using AI and machine learning can streamline several processes and identify places to save money. This may add up and make a huge difference in the long run. Financial considerations also overlap with operational decisions. 

 

3. Operations Management 

The internal operations of a business are just as important as — if not more important than — external sales and operations. Machine learning may help transform your business into a well-oiled machine. 

Some examples of operations that can be streamlined include: 

Typically, operational risk comes about when equipment, clients, or suppliers cause some sort of financial loss for your business. This may be machine malfunctions that are costly to repair, or it may be clients filing claims against your company. In any case, machine learning allows you to analyze past data and figure out how to handle the situations most effectively. 

 

4. Cybersecurity & IT Threat Reduction

With the sophistication of technology comes enhanced risk. Outside threats come from more advanced hackers, viruses, and issues. Machine learning is available to show you where to be on the lookout for these problems. 

Some examples of how machine learning may enhance cybersecurity include:

  • Analyzing third-party cloud services to assess for security risks; 
  • Assessing past cyberattacks and predicting risk in similar situations;
  • Using pattern recognition to mitigate cyber threats
  • Conducting penetration mapping to identify any backdoors or penetrable areas. 

Once you gain an understanding of how cyberthreats come about, you will be better equipped to thwart them. Machine learning is useful in detecting recognizable patterns that suggest cybersecurity issues. If there are gaps in your security system, machine learning will illuminate them. AI also allows you to automate system testing and maintenance, so you don’t miss any valuable time. 

 

5. Human Resources Software & Automation 

Conversations surrounding human resources (HR) software are all over the board. However, it’s undeniable that HR software is upping the recruitment game — but it doesn’t stop there. Machine learning and AI may be able to help HR departments with: 

  • Predicting the potential for success in prospective hires; 
  • Automating and optimizing onboarding processes; 
  • Providing hands-on access to claims information and informing on how to handle employee claims;
  • Analyzing exit interviews for trends that may then be used to inform processes; 
  • Utilizing performance management to identify indicators of low — or favorable — employee performance. 

Saving time doing monotonous HR tasks — like sifting through paper resumes — allows HR personnel to focus on more important aspects. Employees will likely be more satisfied with HR when they have time to give the workforce the focus it deserves. Moreover, machine learning can identify the best people for the job — leading to less turnover and increased revenue. 

 

6. Marketing Analytics & Targeted Campaigns

Marketing professionals know the importance of analyzing their target audience. Instead of simply guessing what consumers will respond to, AI and machine learning predict positive responses based on past data. For example, data analysis in marketing may help businesses with: 

  • Identifying engagement rates on different types of content; 
  • Analyzing what potential customers search for, need, and want; 
  • Identifying gaps in supply and demand; 
  • Predicting threats to reputation and providing suggestions for saving face and handling issues; 
  • Automatically targeting key demographics. 

Learning about potential and current customers gives companies insight into what works and what could use a little tweaking. Search engines, social platforms, and your website may inform how consumers are viewing your brand. Machine learning can take that data and compress it into a digestible format. 

This lets marketing departments tailor ads and public-facing messages to garner more predictable results. Machine learning is a valuable resource for all businesses — large, small, and in between. If used appropriately, the time and money saved are significant. Removing biases and tracking the right KPIs will help your business stay afloat, even in the toughest of times. 

 

Next steps

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

Jul 21, 2021

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