Artificial intelligence (AI) has changed the digital world faster than any other technology in recent memory.   AI is now at the centre of systems that check people's identities around the world, from automated KYC checks to fraud detection and biometric authentication.   Companies like Savora and others use AI-driven technologies to provide verification that is faster and more accurate and meets modern security standards.

But as AI gets better, so do worries about things like algorithmic bias, deepfakes, the misuse of biometric data, privacy violations, and automated fraud.   As a result, governments all over the world are passing new laws to regulate the growth, training, use, and supervision of AI.   These requirements will likely change how identity verification (IDV) providers work in a big way.

This article goes into great detail about how AI regulation will affect identity verification providers, what changes businesses should expect, and why regulated AI might be good for the industry in the long run.

1.  The Rise of AI Rules Around the World

Governments are moving quickly to set the definition of "safe AI" for the first time. Some of the most important areas setting the standard are:

• The EU's AI Act

• AI Executive Orders in the US

• Rules for keeping AI safe in the UK

• The AIDA Act in Canada

• India's Digital Personal Data Protection Act (DPDP Act)

• AI Governance Frameworks in Singapore and the UAE

What do all of these laws have in common?

They all want to make AI systems more accountable, open, and private, especially those that are high-risk.

Most frameworks put identity verification methods that use face recognition, liveness detection, image analysis, or automated decision-making in the "high-risk" category.

This means that big changes are coming.

2.  A "High-Risk AI System" Will Take the Place of Identity Verification

International rules say that any AI that affects user rights, biometric data, or access to financial services is high-risk.

This gives ID verification companies more work to do, such as:

 More openness in AI processing

Providers should be able to explain:

• Why a specific verification option was selected

• How the AI model looked at papers or faces

• What data was used to train the model?

• Was there any human supervision?

This builds trust and reduces "black-box" systems.

Risk evaluations that are needed

• Before using AI, businesses may need to:

• Assessments of bias

• Confirmations of data accuracy

• Assessments of equity

• Checks for security and weaknesses

All risks must be recorded, and providers must show that they have ways to lessen them.

 Logs and audits of compliance

It may be necessary to have third-party audits done often:

• Yearly checks of AI compliance

• Evaluations of data management

• How accurate the reports are and how often mistakes happen

This makes things more mature in terms of operations, even though it makes things harder for the administration.

3.  Stricter rules for keeping biometric data safe

AI-driven identity verification relies heavily on biometrics like faces, fingerprints, voiceprints, and behavioural patterns.

Laws about AI will need:

 More strict rules for biometric storage and encryption

Providers must use cutting-edge encryption to store biometric data, limit access, and put in place strict retention policies.

 Clear approval from the user

Users should know what biometric data is collected, where it will be processed, and how long it will be stored.

Ban on unnecessary biometric processing

Companies that provide identity verification services must show that biometric analysis is required and not just a choice.

Biometrics should only be used for KYC.

You can't use biometric data that was collected for identity verification to market, profile, or train AI systems that aren't related to that.

Because of this, providers are moving toward architectures that put privacy first.

4.  Regulations for AI fairness and bias will change how algorithms work.

One of the main problems people have with AI is algorithmic bias, which happens when AI works differently for people of different ages, genders, or ethnicities.

Regulations will require IDV providers to:

• Look for bias in their models.

• Give out numbers on how often mistakes happen.

• Use balanced datasets to retrain AI.

• Make sure that all groups of people are treated fairly.

Some laws might even require:

"Verification success rates are the same for all demographic groups."

Because of this, providers have to put in place more moral and representational training programs.

5.  Impact on Anti-Fraud and Deepfake Detection

As deepfake technology gets better, identity fraud is changing quickly.   Surprisingly, regulating AI might help IDV providers in this area.

 Regulation makes AI more open

Providers can make users feel more at ease by being open about the deepfake detection methods they use.

 Push forward approved technology that fights deepfakes

AI rules will likely set standard standards for how accurate deepfake detection should be.

AI training that is safer and more responsible

Companies will have to get training data that is ethical and based on consent instead of just scraping random online databases.

This makes it easier to find fraud.

6.  Costs of doing business will go up along with the industry's reputation.

Following AI rules means:

• More paperwork

• Regular audits

• Tools for compliance that cost a lot

• More people to review

• Better infrastructure for safe storage

Even though operating costs will go up, they will be worth it in the long run:

• Customers trust you more

• Higher acceptance rates in regulated fields like fintech, telecom, and banking

• Lowered legal risks

• More of an edge over the competition

Those who offer clear and safe AI will be the leaders in the field.

7.Changes that could be made to the product's design

 Identity verification companies might have to make changes to their technology, like:

  More models that use both AI and people to check things

 There are times when fully automated decision-making might not be possible.

 More proof that the device works

 Instead of sending biometric data to servers, some verification might happen on the user's device.

 Design frameworks that put privacy first

 This means getting as little information as possible to look over.

 More power over the user

 Users may be able to control their identity data through dashboards.

 These changes affect how identity verification systems are designed and used.

 8. More trust, but also more competition

 Rules for AI make things more fair.

 Companies that invest in accuracy, governance, and compliance will do well, while smaller businesses that use AI that is risky or not regulated will do poorly.

 Identity verification becomes:

•  More secure

•  More steady

•  More openness

•  More respectful of people's privacy

 People will trust digital verification systems more once they know what happens to their data.

Conclusion -  Controlling AI is both a problem and a chance to improve identity security in the end.

 AI is used to check people's identities these days, but it can also be risky if the right rules aren't in place. Regulations are meant to make sure that AI systems are fair, open, and safe, not to stop new ideas from coming up.

This new era gives identity verification providers:

• More responsibility

• Better handling of data

• Better protection for biometrics

• Better fairness

• More trust from customers

As well as meeting new rules, companies like Savora and other serious IDV providers that invest in compliance early will be at the forefront of the future of safe online access.

Regulating AI will definitely help with checking people's identities.