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4 Ways AI Can Improve Enterprise Risk Management

artificial intelligence risk management

Can you think of 4 ways that AI can improve enterprise risk management? 

Well, here are four ways we believe it will.

According to a study done by Allied Market Research, “The global risk management market size was valued at $6,258.40 million in 2018, and market forecast is projected to reach $18,504.22 million by 2026, growing at a CAGR of 14.6% from 2019 to 2026.”

It is clear that innovating in this field stands to make companies lots of profits, not to mention the chance to make a real impact on humanity. Here’re a few amazing case studies of companies who hired DevTeam.Space to build simular products:

  1. Property Ownership Software – Housing Ownership Verification Program
  2. Neural Network Library – Machine Learning Application
  3. Air Sign – Machine Learning Program For Air Signature Recognition

Contents

Enterprise risk management: An overview
The global market for ERM software
What Is  “Artificial Intelligence” (AI)
AI for enterprise risk management: A perfect match!
Implementing an AI solution for ERM
Final thoughts

Enterprise risk management: An overview

enterprise level risk management

A key function in any enterprise including your organization, ERM is about identifying key risks. You use ERM to assess, prioritize, and mitigate these risks.

You use the insights from ERM to communicate with internal and external stakeholders including investors. Read more about ERM in “Enterprise Risk Management (ERM)”.

Naturally, enterprises turn to technology solutions to implement ERM. The global ERM market is growing. It’s expected to reach $5.8 billion in 2027 from $3.9 billion in 2019, with a CAGR of 5%. Read more about this projection in this “Transparency Market Research” report.

You could find it hard to implement ERM though! This is due to the following challenges:

  • Inconsistent definition of risks: Enterprises have silos within them and they don’t define risks using a consistent framework.
  • Inconsistent risk assessment practices: Another impact of the silos within enterprises is that risk assessment practices are inconsistent.
  • The complexities of reporting risks: The outcomes of ERM are highly visible and many stakeholders see them. Meaningful reporting is key, however, it’s hard to create reports that are relevant to all stakeholders

Read “The 3 common challenges of ERM” to learn more about these challenges. Before we talk about how AI can help, let’s quickly review what AI is.

The global market for ERM software

global market for ERM software

You can see how critical ERM is, don’t you? Naturally, enterprises tend to depend on software solutions to run this key function.

The following are a few key players in the ERM software market:

  • IBM;
  • SAP;
  • Oracle;
  • Capgemini;
  • LogicManager;
  • AGCO;
  • SAS;
  • MetricStream;
  • Enablon;

Read more about this market in this MarketWatch report.

The global ERM market is witnessing impressive growth. Industry observers expect that this market will reach $5.8 billion in 2027 from $3.9 billion in 2019, with a CAGR of 5%. Read more about this projection in this “Transparency Market Research” report.

What Is “Artificial Intelligence” (AI)

Why is AI important? Well, it’s a technology that enables computer systems to perform tasks that require human intelligence. AI-powered computers “learn” from “experience” and perform human-like tasks better over time.

AI is an interdisciplinary branch of computer science, and parts of it are commercially available as technologies. Read about AI in our guide “A giant leap for humankind: Theory of Mind AI”.

There are various capabilities within AI, e.g.:

  • “Machine Learning” (ML): It includes deep learning, supervised learning, and unsupervised learning.
  • “Natural Language Processing” (NLP): This capability encompasses content extraction, classification, and machine translation.
  • “Vision”: It includes capabilities like image recognition and machine vision.
  • “Speech”: This capability includes speech-to-text and text-to-speech.

This isn’t an exhaustive list, and you can read more about AI in our guide “AI solutions to digital identity”.

As you would expect, the global market for AI is growing rapidly. This market was worth $20.67 billion globally in 2018, however, it’s expected to reach $202.57 billion by 2026. According to a Fortune Business Insights report, the market for AI will see a CAGR of 33.1% during the 2019-2026 period.

AI for enterprise risk management: A perfect match!

Wondering how AI can help with ERM? AI indeed can improve ERM in several ways, e.g.:

  • AI-powered tools can provide general guidance and assistance to risk management professionals, which saves costs.
  • The ERM process needs to use the enterprise helpdesk ticket data for insights into what kind of challenges the customers, partners, and employees face. Now, the helpdesk ticket data is massive, and it can be hard to gain insights from it. AI can smartly categorize the incident data, therefore, ERM tools can extract insights easily.
  • AI can study a large volume of enterprise helpdesk ticket data and recommend mitigation measures.
  • Enterprise risk management professionals can use AI to predict the likelihood of incidents. They can also use AI to predict the financial loss occurring from such incidents.
  • AI-powered tools make a big difference in identifying breaches in security and/or other business controls. Enterprise risk management requires audit and compliance managers to find such breaches, and AI can make their work easier.

Read “How can artificial intelligence enhance enterprise risk management?” to learn more.

AI for enterprise risk management: A perfect match!

Wondering how AI can help with ERM? AI indeed can improve ERM in several ways, e.g.:

  • AI-powered tools can provide general guidance and assistance to risk management professionals, which saves costs.
  • The ERM process needs to use the enterprise helpdesk ticket data for insights into what kind of challenges the customers, partners, and employees face. Now, the helpdesk ticket data is massive, and it can be hard to gain insights from it. AI can smartly categorize the incident data, therefore, ERM tools can extract insights easily.
  • AI can study a large volume of enterprise helpdesk ticket data and recommend mitigation measures.
  • Enterprise risk management professionals can use AI to predict the likelihood of incidents. They can also use AI to predict the financial loss occurring from such incidents.
  • AI-powered tools make a big difference in identifying breaches in security and/or other business controls. Enterprise risk management requires audit and compliance managers to find such breaches, and AI can make their work easier.

Read “How can artificial intelligence enhance enterprise risk management?” to learn more.

Implementing an AI solution for ERM

Now that you can see the value of AI for ERM, you are likely wondering about implementing an AI solution to transform this key business function. You need to focus on the following:

1. Define the scope of your AI-powered ERM solution development project

Onboard a competent PM with the experience of AI development projects. You also need a capable IT architect and a team of knowledgable business analysts (BAs). Lead this team through the following activities:

  • Identify the areas in your ERM process that you want to transform.
  • Choose the ERM process steps where you will implement an AI-powered solution.
  • Determine the SDLC model for your project. Use Agile since experts find that it’s helpful for AI development. Want to learn more about Agile? Check out our guide “What is software development life cycle and what you plan for?”.
  • Analyze your requirements and finalize them. For this, you need to have intense discussions with the relevant stakeholders.
  • You also need to finalize your “Minimum Viable Product” (MVP) scope.

Wondering how to finalize the requirements for your ERM solution including your MVP? Our guide “5 tips to create a sleek MVP” can help.

2. Planning an AI solution for ERM

Plan your AI development project thoroughly. This requires you to do the following:

  • Identify the areas in your ERM process that you want to transform. You need to onboard a competent PM with the experience of managing an AI development project. Onboard a competent IT architect and a knowledgable team of business analysts (BAs).
  • Choose the ERM process steps where you will implement an AI-powered solution.
  • You will need to “train” your AI solution with the help of a vast dataset. Identify this dataset.
  • Identify the AI capabilities you need to implement, e.g., machine learning (ML), natural language processing (NLP), etc.
  • Determine the SDLC model for your project. Use Agile since experts find that it’s helpful for AI development.
  • Analyze your requirements and finalize them. You also need to finalize your “Minimum Viable Product” (MVP) scope.
  • Plan your technical design, development, and testing phases.
  • Finally, spend sufficient time to plan your deployment and maintenance activities.

Wondering how to plan an AI development project? Our guide “AI development life cycle: explained” can help.

3. Choose a cloud platform

You want to expedite your project, don’t you? You can do that if you use a cloud computing platform instead of managing IT infrastructure yourself.

What kind of cloud platform should you choose? Well, this depends on your project scope. If you are developing a web app with AI components, then I recommend that you use a “Platform-as-a-Service” (PaaS) platform like AWS Elastic Beanstalk.

PaaS platforms manage the cloud infrastructure, operating system, middleware, and runtime environment. This allows you to focus on development, moreover, PaaS platforms offer robust DevOps tools and auto-scaling solutions. I have explained their advantages in “10 top PaaS providers for 2020”.

On the other hand, are you also developing a mobile app with AI capabilities? Save time spent in building and managing the mobile backend so that you can focus on the front-end and the business logic.

I recommend that you use a “Mobile-Backend-as-a-Service” (MBaaS) platform like AWS Amplify. MBaaS platforms manage the cloud infrastructure and persistent storage, therefore, you don’t need to build and manage the mobile backend. Such platforms make it easy to integrate APIs, as I have explained in “How to choose the best Mobile Backend as a Service (MBaaS)?”.

4. Decide on the technology stack

Which technologies should you use? Thanks to the growing popularity of AI, you have quite a few options!

You could use one of the AI development platforms that offer key AI capabilities like ML and NLP on the cloud. A few examples are as follows:

  • Microsoft Azure AI platform;
  • Google Cloud AI platform;
  • IBM Watson;

I have explained their advantages in our guide “The best Artificial Intelligence software development tools of 2020”.

On the other hand, you could choose to develop your AI software from scratch. You can cater to your customized requirements this way. Python is a popular language for AI development, and it has many libraries to help with AI/ML programming. If you want to learn more about this language, then check out our guide “Julia vs Python: Can this new programming language unseat the king?”.

If you are developing a web app, then I recommend you to use Node.js. It’s a highly popular open-source runtime environment, and you can create scalable and performant web apps with it. Read our guide “10 great tools for Node.Js software development” to learn more about its advantages.

Developing mobile apps too? I recommend that you develop native Android and iOS apps. For native Android development, consider using Java. This security-friendly language has been a mainstay for native Android development for long.

I recommend that you use Swift for native iOS development. This popular language enables you to code scalable and performant iOS apps, moreover, you can avoid common coding errors. Our guide “How to migrate your Objective-C project to Swift?” explains its advantages.

5. Find the right people

You need the right people to achieve success in a project like this, don’t you? Remember that AI development is a niche area, and you could find it hard to onboard competent people. Need help with this? We at DevTeam.Space can help, as I have explained in our guide “How to find a good software developer”.

While you will need to follow the right development and testing practices, does that suffice? It doesn’t! You need to implement a systematic code review process to find defects early in the cycle.

Finding experienced reviewers can be a challenge though. Once again, we at DevTeam.Space can help, and you can read about our capabilities in “Why choosing DevTeam.Space to review your code can ensure your software product is a success”.

Final thoughts

An AI development project to transform your initiate an artificial intelligence enterprise risk management feature can be challenging since AI is a niche area. It will surely be a high-visibility project. I recommend that you engage a reputed software development company for such a project. Check out our guide “How to find the best software development company?” to find one.

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