#1 Unlocking the value of unstructured data through machine learning

Dr. Alexey Drozdetskiy, Head of Data Science for Kainos explains why machine learning is key to boosting business performance by extracting, contextualising, and structuring unstructured data.
Date posted
1 March 2022
Reading time
6 minutes
Alexey Drozdetskiy
Head of Data Science ·

The unstructured data challenge 

Businesses today have to deal with a countless number of documents rolling in on a daily basis. It is impossible to effectively manage and analyse such vast amounts of information by hand. When trying to make sense of unstructured information is too much of a headache, machine learning (ML) provides the solution needed to take control of these valuable data streams and reveal their insights.  


The unstructured data challenge 

Unstructured data can come in many forms – it could be receipts handed to bank tellers or accountants, emails between suppliers and customers, online applications submitted by insurance claimants, handwritten patient records or police notes, imagery and scans, business invoices, and PDFs – the list is endless.  


Such documents can provide rich insights, but often take a lot of work to make them analysis ready. It is complex and difficult to retrieve and integrate, as many analytics tools are only able to ingest data in structured formats – such as spreadsheets and databases.  


With the volume of data increasing exponentially, you would need an army of humans to manually search and capture all the relevant information that could result in business changing insights. Such manual data capture is extremely time-consuming, costly and prone to error – meaning insights are often outdated before they get chance to be processed. This means many businesses ignore these records in analysis processes, and the quality insights hidden inside  remain locked away.  


International Data Corporation estimates that 80% of global data will be unstructured by 2025 and nine out of ten chief data officers (CDOs) believe the management of unstructured datasets must be addressed in the next year, but few have considered how to make the transition. ML bridges the gap between unstructured information and actionable data analysis, achieving an ROI no matter an organisation’s technological maturity level. 


What is machine learning, exactly? 

ML, a branch of artificial intelligence (AI), operates under the hood in many digital services we use today – including search engines, social media, e-commerce, and voice assistants. Automated, ML-powered systems have virtually unlimited computing power, resources, and memory in comparison to human counterparts and can be taught to “connect the dots” during data analysis.  


By harnessing algorithms and computational models to recognise speech, text, visuals, and patterns – behavioural or otherwise – ML-powered systems can issue alerts, make real-time recommendations, or take action based on statistical patterns. Ultimately, it takes away the burden from manual data entry and analysis services. Not only can this cut operational costs and reduce errors, but organisations can also unlock insight for competitive advantages, obtain a more transparent view into existing business practices and customer relations management, and respond rapidly to changes in the market.  


Transforming claims and reducing insurance fraud with ML 

There are many industries where ML can help – especially paper-heavy, record intensive industries, such as insurance. Here, ML can be applied to unstructured inputs in insurance processes – including claim documents, phone call records, photos and videos, police reports, witness statements, and even the applicant themselves – to verify requests for compensation and to detect fraud. 


ML can pull claim records together to detect patterns in applications . There are also fraud and risk signals that call handlers can be made aware of, such as if the individual has previously made fraudulent bids for compensation.  


ML is so valuable because it can alert handlers in real-time while they are on the phone to an applicant. Checks can run automatically in the background and if red flags appear, the human operator can be warned and will be equipped with the right knowledge in what the next steps to take should be.  


ML in action – how Kainos has helped customers to apply ML to reduce the burden of document processing  

At Kainos, we’ve helped numerous clients embrace ML successfully. In one example, our team helped one organisation that needed to digitise millions of historical documents. Many of these records were handwritten, difficult to decipher and penned by authors over the course of 100 years. While trying to perform this task by hand could take a lifetime, ML made the process far more effortless.  


We also worked with HM Land Registry (HMLR), which manages land and property ownership records to the value of £7 trillion. HMLR needed to streamline casework and the handling of deed comparisons that were being conducted manually. The Kainos team developed a ML model that was taught to understand deeds, using text recognition and image-based analysis to recognise legal language, reducing document review times by half.  


In another project, we worked with a healthcare provider that needed an automated solution to “read” prescriptions and detect  trends in sickness and medical fraud. The model we built is capable of mimicking the human ability to learn, gradually improving its accuracy and analysis capabilities over time.  


Accelerate growth with ML 

Adopting machine learning to reform existing business processes doesn’t have to be daunting. Whether a company is a SME or a member of the Fortune 500, the need to analyse and extract value from unstructured data is critical. Kainos has the technical expertise to build bespoke solutions that accelerate business growth, and we continue to focus our efforts on the machine learning space.  


We were shortlisted in the Outstanding AI ML Project Awards category of Computing AI & Machine Learning Awards for work with our previous clients. If you would like to know more about making machine learning work for your organisation, contact us today using the form below.  

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About the author

Alexey Drozdetskiy
Head of Data Science ·
Alexey is Head of Data Science at Kainos. He has implemented and led multiple successful projects from Pre-Sales and Advisory to Production and Managed Services for clients ranging from small organisations to FTSE100 companies. He holds a PhD in Particle Physics with a 20+ year career in science and big data. Alexey has authored and co-authored dozens of refereed papers including development of some widely used Machine Learning and statistical algorithms.