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AI Solutions To Digital Identity

When we at DevTeam.Space developed a machine learning (ML) algorithm for the recognition of air signatures with the help of mobile devices for “AirSign”, we gained first hand knowledge of how businesses can benefit from Artificial Intelligence (AI)-powered digital identity solutions.

You can use them in your organization too.

The first step on this road is for you to get a good understanding of what AI based solutions to digital identity there are.

This is exactly what I will explain in this guide.


Why AI in digital identity?
A brief introduction to AI
The importance of AI
How can AI help with digital ID management?
Examples of using AI for digital identity verification
Building an AI based solution to digital identity
Planning to launch an AI based solution to digital identity?

Why AI in digital identity?

You have likely come across several valuable AI use cases already, e.g., digital personal assistant, customer service, fraud prevention, etc. I have earlier described examples of AI at work in “10 best AI apps of 2019”.

However, you might be wondering why we are talking about AI in digital identity, therefore, I will set the context first. The importance of digital identity in today‘s world can hardly be overstated, however, securing one‘s digital identity is a challenge.

In 2018, incidents of identity theft have impacted 14.4 million people in the US alone! Cyber-attackers stole over 791 identity records in the US in 2016 alone, and losses due to “Account Take Over” (ATO) attacks amounted to $4 billion in 2018. Read more such sobering statistics in “Identity theft stats & facts: 2018 – 2019”.

Technology giants like Google and Facebook have amassed a tremendous amount of personal data of their users, and theft of digital identity exposes their users to risk. Read more about this in “The new age of digital identity & its challenges”.

Online users control little of the data that these technology giants extract about them, which compounds the risk. You can learn more about this in “Your digital identity has three layers, and you can only protect one of them”.

Digital identity management needs better technology solutions! This prompts us to search for robust technologies, consequently, technology experts have researched on how AI can help with digital identity management.

A brief introduction to AI

Before understanding the role AI can play in digital identity management, let‘s first briefly understand AI. Artificial Intelligence is an interdisciplinary branch of computer science with a significant history of research and development.

While research on AI continues, a good deal of this science has been commercialized, and it‘s an impactful technology. AI allows computers to perform tasks that would normally require a human being. Computers powered by AI “learn” from “experience”, and they improve over time by learning from new information.

There are various capabilities within AI, as I have explained in “AI development life cycle: explained”. These capabilities are as follows:

  • Machine Learning (NL): This capability includes deep learning, supervised learning algorithms, and unsupervised learning algorithms.
  • Natural Language Processing (NLP): NLP includes content extraction, classification, machine translation, etc.
  • Vision: Image recognition, machine vision, etc. fall within this capability.
  • Speech: This capability includes speech-to-text and text-to-speech.

Expert systems, planning, robotics are the other key AI capabilities.

There are four categories of AI, and these are as follows:

  • Reactive AI: Systems powered by reactive AI can‘t deal with past or future, and they can only accomplish the present task at hand.
  • Limited memory AI: Systems equipped with limited memory AI can refer to events from a short time ago, and self-driving cars are a good example of this.
  • Theory of Mind AI: Currently under research and development, systems equipped with the Theory of Mind AI can understand the emotions of people and other AI-powered systems.
  • Self-Aware AI: Systems powered by Self-Aware AI will be able to understand themselves, however, this category of AI is still at an early stage of research.

Read more about these categories in “A giant leap for humankind: Theory of Mind AI”.

The importance of AI

AI has applications in a wide range of sectors, e.g., agriculture, banking, manufacturing, retail, healthcare, etc. The technology can significantly improve many functionalities in an organization, e.g., supply chain management, IT infrastructure security management, customer support, etc. I have explained its importance in “The best artificial intelligence software development tools of 2019”.

Consider the example of using AI in facial recognition software. This AI use case can help security agencies to keep our borders safe, as I have explained in “How to build facial recognition software”.

Publishing houses can use ML to classify books, as I have explained in “How to build a Machine Learning filing system to classify books”. Medical chatbots can help healthcare services providers to serve their patients better, as you can read in “Benefits of building medical chatbots for your healthcare business”.

At the other end of the spectrum, AI and ML are transforming the software development industry, as I have earlier explained in “Machine learning in future software development”. Gartner projects that by 2022, AI will generate business value worth $3.9 trillion, as you can read in “Roundup of machine learning forecasts and market estimates for 2019”.

How can AI help with digital ID management?

There are various ways in which AI can help with digital ID management, and these are as follows:

  • Determining the true identity of an individual becomes easier with AI.
  • A facial recognition-based identity verification system is hard to circumvent.
  • ML algorithms can detect forgery attempts concerning identity documents.
  • AI-powered identity verification systems are faster than manual verification processes.
  • Identity verification solutions utilizing AI and ML can process a large number of ID verification requests quickly.

You can read more about this in “How artificial intelligence can usher a new wave of identity verification services?”.

Examples of using AI for digital identity verification

The following are a few examples of AI based solutions to digital identity:

Continuing research and development into AI will likely bring a paradigm shift in identity and access management (IAM). In a not-so-distant future, AI could securely identify users by using sights and sounds, and it won‘t rely on predefined credentials. Read more about this in “Could AI improve identity management and security?”.

Building an AI based solution to digital identity

The following are the steps needed to build an AI based solution to digital identity:

1. Agree on a project scope

To start with, you need to induct a project manager (PM), an IT architect, and a team of business analysts to define the project scope and the business requirements. This team needs to work with the business stakeholders to define the business requirements.

E.g., you might launch a web app for digital identity verification with a facial recognition feature. Additionally, you could plan to make the app efficient using AI.

2. Zero in on a project methodology

Experts have observed that the Agile development methodology fits well with artificial intelligence development projects, and you can read more about it in “5 ways to improve AI/ML deployments”. I recommend that you use Agile to manage this project.

3. Formulate a development approach

I recommend that you use the following development approach:

  • Use a managed cloud services platform so that you don‘t need to manage IT infrastructure.
  • Expedite your project using application programming interfaces (APIs) to implement the core features.
  • Improve your test coverage using a test automation aid.

You can read “What is the best development approach to guarantee the success of your app?” to understand why this approach matters.

4. Estimate the project

Your PM should prepare a detailed project estimate as part of the project planning process since this would help the project sponsor to give a “go ahead” for the project. The PM should estimate the cost for the development manpower, IT infrastructure, development tools, etc. Our guide “How much does it cost to develop an AI solution for your company?” can help you with this exercise.

5. Form a project team

You now need to form the rest of the project team with the following roles:

  • UI designers;
  • AI software developers;
  • Web developers;
  • Testers;
  • DevOps engineers.

AI and ML skills are at a premium, therefore, you could find it hard to hire an AI programmer. Given that AI programming tends to be complex, you should induct a field expert development team. Read “Freelance app development team vs. field expert software development teams” to learn more about this.

6. Sign-up for AWS Elastic Beanstalk

You would like your team to focus on development instead of IT infrastructure management, therefore, I recommend that you use AWS Elastic Beanstalk. It‘s the Platform-as-a-Service (PaaS) offering from AWS, and it offers several advantages, e.g.:

  • Elastic Beanstalk manages the cloud infrastructure, networking, storage, operating system, middleware, and runtime environment. This frees you up so that you can concentrate on the web app development.
  • You will find it easy to integrate databases and 3rd party APIs with your web app.
  • It‘s easy to use the DevOps tools that Elastic Beanstalk offers, moreover, you can use its auto-scaling capabilities to scale your web app easily.

7. Find appropriate API solutions

The features of your proposed AI based digital identity solution will determine which APIs you should use, and I will explain this with a few examples.

Example #1: Amazon Rekognition for facial recognition

If your proposed app includes facial recognition, then Amazon Rekognition is a good API option for you. It‘s a robust API solution to add image and video recognition features in your app. Amazon Rekognition offers the following features:

  • It can identify objects, people, scenes, etc. from images and videos.
  • Amazon Rekognition offers accurate facial analysis and recognition, moreover, it‘s fast and scalable.
  • This API solution uses robust deep learning capabilities, moreover, it‘s an easy-to-use API.

Read “Getting started with Amazon Rekognition” to learn how you can use it, and review the Amazon Rekognition pricing plans before you buy.

Example #2: IDnow for faster verification of identity documents

Does your proposed app promise faster verification of identity documents? IDnow offers a robust API solution for this, and it‘s powered by AI, facial recognition and ML.

IDnow is an established provider of AI-powered digital identity solutions, e.g., they already work with major financial services institutions for KYC verification. This platform offers several advantages, e.g., faster onboarding, compatibility with a wide range of devices, security, performance, and scalability. Read the IDnow API documentation to learn how to use this solution.

8. Sign-up for pCloudy to improve your test coverage

Your proposed web app should work with all available browsers and their different versions. You need to test the web app against different browsers for that, therefore, sign-up for pCloudy. It offers a wide range of browsers and mobile devices on the cloud, which helps to test web and mobile apps.

9. Design a user-friendly UI

Your app needs a user-friendly UI. I recommend that you follow the right guidelines to design one, and “User interface design guidelines: 10 rules of thumb” is a good starting point.

10. Web app development

I recommend that you code the proposed web app using Node.js, which is a popular open-source runtime environment. It‘s scalable and performant.

You should use Eclipse IDE for coding the app, however, you also need the “Enide (Studio) 2015 – Node.js, JavaScript, Java and Web Tools” plugin.

You need to read “Adding a database to your Elastic Beanstalk environment” to understand how you can add databases to your app on AWS Elastics Beanstalk. Integrate the relevant APIs with your code, and test the app.

AWS Elastic Beanstalk offers detailed guidance on how to deploy your web app. Read “Deploying Node.js applications to AWS Elastic Beanstalk” to learn more.

Planning to launch an AI based solution to digital identity?

AI can add significant value to your digital identity solution, however, developing such a solution can be hard. Consider engaging reputed AI development companies for such projects, and read our guide “How to find the best software development company?” to find such a company.