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Machine Learning in 2020: Top Trends and Identity Verification Benefits
5 months ago by Angelique Assaf

Machine Learning in 2020: Top Trends and Identity Verification Benefits

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Today's market is growing continuously more competitive and technologically advanced. How do companies compete? One answer can be found through a powerful aspect of artificial intelligence: machine learning.

Machine learning can help companies get an advantage over their competitors by granting a better understanding of the most vital aspect of any purchase: the customer. Imbued with that knowledge, companies can offer better customer experience, provide highly customized services and products, and streamline their operations.

Another critical way business can succeed in a competitive market is through a compliance process, such as KYC and identity verification. Machine learning is a crucial part of this, as it helps businesses better understand the companies and individuals that will be their new business partners.

However, machine learning and AI is an industry of constant change, so it’s challenging to keep up with the newest developments and see how they will affect compliance procedures like identity verification. Luckily, we’ve done the dirty work and highlighted three key machine learning trends in 2020 for you, alongside an overview of the impact they will have on identity verification.

Advancements in NLP

NLP, or Natural Language Processing, is one of the most common domains in machine learning.

However, even though machine learning and NLP are commonly used alongside each other, they have different purposes. Machine learning describes systems that learn from experience, whereas NLP technologies are programmed to understand, study, and even use the human language.

In the last few years, helped by improvements in computing capacities, researchers have made vast improvements in pre-trained language models that help enhance NLP capabilities. Language models are now lighter but retain their high performance, and they can pre-train from a single, larger dataset that can be tweaked and adapted to perform various NLP tasks. This progress has made using NLP more cost-effective, quicker, and simpler.

However, despite this progress, researchers aim to make language models even more polished and sophisticated. Here’s some trends to take note of:

·         Fine Tuning “Pre-Training” for NLP. A key area of NLP research in the last few years has been Transfer Learning. Transfer learning centres around the storing of knowledge earned while solving one problem and utilizing that knowledge to solve similar problems. This research has led to the development of a new generation of pre-trained language models including ULMFiT, CoVe, ELMo, OpenAI GPT, BERT, OpenAI GPT-2, XLNet, RoBERTa, ALBERT, to name a few. Despite the advancements to NLP, scientists still must work on reducing its cost and the large annotated databases it requires.

·         Linguistics’ Role in the Enhancement of NLP.  Experts believe that by employing context and linguistics in deep learning, it will enable NLP models to interpret data better.

·      Neural Machine Translation. The addition of enhanced neural network architectures, visual context, and new tactics in semi-supervised and unsupervised machine translations have all boosted the progress of neural machine translation. Due to their success, these are technologies that we will continue to see for 2020 and beyond.

How can NLP be applied to identity verification?

By implementing NLP technology within the identity verification process, you can enhance the processing of data, the software's comprehension, and improve its ability to authenticate data from the structured and unstructured databases used in identity verification and KYC protocols. 

Computer Vision

Computer vision (CV) are systems that rely on AI technology to understand the content of images or videos. Computer vision is used in the recognition and tracking of objects, and it is widely used within multiple industries, including but not limited to retail, banking, automotive, and security. Although recent developments in computer vision architectures and approaches have improved the technology, there are still many upgrades that are currently being explored in 2020:  

·      3D Is Leading the Pack. One of the most widely researched areas of computer vision in 2019 was 3D – specifically the recreation of a 3D world from 2D images. A prime example of this is Google's new software for generating depth maps.

·        Computer Vision and NLP Successfully Working Together. According to Forbes “The latest research advances enable robust change captioning between two images in natural language, vision-language navigation in 3D environments, and learning hierarchical vision-language representation for better image caption retrieval and visual grounding.”

How can computer vision be applied to identity verification?

Computer vision is utilized by technologies that offer image recognition applications that verify the authenticity of identification documents, such as passports and driving licenses. An example of this is the company Mitek Systems. Through pictures of crucial identification papers, Mitek Systems software can categorize what type of document it is, identify what the essential data is according to the kind of record it is, and validate its authenticity.

This technology will continue to be a vital tool for the banking and finance industry, where companies must verify the identity of their customers as part of compliance laws. With this technology, not only can banks quickly and easily confirm their client's identity, but they can also add another level of security to protect their client’s personal information and money. 

Hybrid Deep Learning Takes the Lead

Deep learning refers to a subclass of machine learning that uses neural networks, a concept taken directly from the human brain to learn from massive data sources. For the year 2020 and beyond, deep learning is poised to join forces with other frameworks that will enhance its ability to learn.

By combining deep learning with other AI concepts such as causal reasoning, domain knowledge and constraints, and symbolic reasoning, researchers are hoping to create a more robust and efficient AI. That will be effective in solving problems that have little to no data available or when it is challenging to apply generalization.

What does this mean for identity verification?

AI is already a crucial tool in identity verification—creating a more powerful AI software will further streamline the process and make identity verification quicker and more accurate. This will help businesses to mitigate the risk of entering new markets and partnerships, especially in smaller countries where compliance laws are laxer, and the corporate databases are not as detailed, not as accurate, or simply don't exist yet. 

Conclusion

Are you interested in knowing more about a potential business partner? At Cedar Rose, we offer high-quality, tailored due diligence research, including a database that’s based on AI technology that allows you to access millions of companies, shareholders, and individuals. With our database’s instant identity verification, you can effortlessly minimize risk and ensure that you are dealing with trustworthy companies and individuals.  Give us a call today at (+357) 25 346630 to speak with one of our representatives.

  • machine learning
  • 2020
  • computer vision
  • learning algorithm
  • identity verification
  • neural machine translation