We are delighted thatÌýÄ¢¹½¶ÌÊÓƵÌýwill be one of the companies involved, the aim of the event is to discover the innovation taking place across the UK in the professional and financial, insurance, accountancy and lawÌýsectors.Ìý

Kainos,ÌýAdoreboardÌýand Analytics Engines are in amongst the few other companies also representing Northern Ireland in the AI for Services Tour.ÌýÌýÄ¢¹½¶ÌÊÓƵ Head of AI, Dr Fiona Browne will be pitching at the event.ÌýWe thought it would be a good idea to catch up with Dr Browne ahead of the event to find out what it’s all about!Ìý

Hi Fiona! Could you tell me more about the event and whyÌýÄ¢¹½¶ÌÊÓƵÌýis involved?Ìý

The AIÌýforÌýServices event isÌýa UK-wide eventÌýhostedÌýby KTNÌýInnovate UK and we are part of the NI cohort. TheÌýevent is a roadshow, which willÌýprovide the opportunity forÌýcompanies from all the different regions to highlight what they are doing in terms ofÌýinnovationÌýandÌýAI and how these can addressÌýareas within the various sectors.ÌýThe roadshow will also allow each of the companies to pitchÌýto organisations in different sectors including Accountancy, Insurance and Financial Services.

Fiona, you will be giving one of these pitches at the event. What can you tell us about it?Ìý

All the regions have a chance to provide aÌý7-minuteÌýpitch. We will be describing whoÌýÄ¢¹½¶ÌÊÓƵÌýareÌýand whatÌýwe specialise in (Data Quality and Matching). We will be focusing on a particular use case, which is related to Onboarding and the role of entity matching within this process, highlighting the recent work we have done in this area. We will be highlighting the data quality required before the matching process occurs, but also how we have augmented our matching process with machine learning.ÌýÌý

If you could pick one key takeawayÌýthat you would want people toÌýgetÌýfrom the pitch, what would it be?Ìý

I think the key message to takeaway is that Machine Learning (ML) has a role to play inÌýaddressing manualÌýtime-consumingÌýtask and when applied to the correct applications, it can make efficiencies savings. However, goodÌýML is built on quality data and effort is needed to ensure that you have aÌýreproducibleÌýdata quality pipeline in place.ÌýAtÌýÄ¢¹½¶ÌÊÓƵÌýwe pride ourselves on ourÌýdata quality and matching technology and have innovated in these areas.ÌýWe areÌýreally excitedÌýabout the developments we are making, and we can’t wait to tell you more!Ìý

Ä¢¹½¶ÌÊÓƵÌýwill be representing NI. Do you think that the talent here locally and the technological developments are matching up to the rest of the UK?Ìý

Yes! There’s a real focus on Artificial Intelligence and FinTech within NI.ÌýThe country may beÌýsmallÌýin sizeÌýbut in terms of capabilities itÌýoffers great solutions.Ìý

What do you hope to be the biggest takeaway for attendeesÌýon the whole event?Ìý

The idea of this event isÌýfor companiesÌýwithin sectors such as finance, insurance, law and accountancy who are embarking or on their wayÌýto theirÌýdigital transformationÌýjourneyÌýto connect with companies that offerÌýinnovative solutions.ÌýAt Ä¢¹½¶ÌÊÓƵ we want to better understandÌýthe bottlenecks andÌýpain pointsÌýthat these companies in these sectors are facing and offer a solutionÌýthat addresses these. We hope to deepen our specialist knowledge in understanding the current challenges in the industry so that we can tailor our technology to solve real business problems. WeÌýwillÌýshowcase ourÌýself-serviceÌýdata quality and matchingÌýsolutionsÌýhighlighting theÌýcontinual developments we have made with machine learning to augment the matching process.Ìý

It is also a great opportunity to leverage our presence in these sectors as we are primarily linked to financial and governmental. Accountancy, Law and Insurance are sectors that we haven’tÌýtraditionally marketed toÌýbut have similarÌýareas to address such as compliance to regulation and common data management challenges.Ìý

What would you like the audience to share?

We will highlight what our solution is and what we do, but we want to understand better the pain points. Where do the difficulties lie?ÌýIs it extracting knowledge from textual sources of information? Or is it issues with integrating different data sources? Or is it issues with adhering to regulations?ÌýÌýIt will be good to hear first-hand from these organisations.

Are you looking forward to hearing any particular pitch on the day?Ìý

I am looking forward to hearing them all. Particularly because all the companies are very different, it’ll be interesting to hearÌýmore about their solutions and the innovations that they areÌýoffering.Ìý

How can attendees be able to get in touch with you?Ìý

YouÌýcanÌýregister asÌýa delegateÌýto hear the presentationsÌý. Then, Innovate UK is using a platform called Meeting where 1:1 meeting can be bookedÌýbetweenÌý12:30-2 pmÌýwithÌýcompanies.Ìý

The event is sure to be a good one,Ìýwe are excited to be involved. We are most excited to learn more about the different sectors!ÌýKeep an eye on the KTN social media pages for updatesÌýon the event. KTN also has an events archive where you can listen to past events if you have missed them, check it out .

VisitÌýhereÌýfor more by Ä¢¹½¶ÌÊÓƵ, or find us onÌý,ÌýÌýorÌýÌýfor the latest news.Ìý

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We started by looking at why data matching is a key part of any AML & KYC process. It’s made more complex by the different standards, languages, and levels of quality in the different data sources on which firms typically rely on. It’s expensive too: a recentÌýÌýstates that some firms are spending up to $670m each year on KYC.Ìý

As the week went on, we looked at some of the key areas where Ä¢¹½¶ÌÊÓƵ makes a real difference in helping firms to reduce manual effort, reduce risk, and bring down the extremely high cost of client onboarding. 

We then looked at the impact of theÌýEU’s fifth AML directiveÌýand how firmsÌýare able toÌýautomate their sanctions screening with theÌýsanctions match engine.ÌýÌý

We also exploredÌýhow we support efforts to reduce risk and financial crime involving the clever tech we’ve used to transliterateÌýbetween characterÌýsets andÌýperformÌýmulti-languageÌýmatching.Ìý

Finishing up, we shared our talk with the EDM Council that explored how AI can make a real difference to the story. Bringing even more predictive capabilities to human effort means that finding those edge cases, don’t have to wait until all the obvious ones have been ruled out.ÌýWe also composed a piece entitled ‘Lifting the lidÌýon the problems thatÌýÄ¢¹½¶ÌÊÓƵÌýsolves’, if you missed it out can check it out here.Ìý

If you missed any of the pieces we shared this week, feel free to read them on our DataBlog or on our social media platforms.  

In other news this week, our very own Head of AI, Dr Fiona Browne contributed to the . This session discussed the huge AML fines faced by the banks over the last number of years.

At Ä¢¹½¶ÌÊÓƵ we are a company that helps banks gain quality data – a tool that is equipped to fight fraudsters and money launderers. Fiona was able to share her experience as Head of AI at Ä¢¹½¶ÌÊÓƵ to shed light on how banks can arm themselves sufficiently to allow them to stand up to increasing regulatory and technological complexity. 

Ä¢¹½¶ÌÊÓƵÌýprovides the tools to tackle these issues with minimum IT overhead, in a powerful and agile way.Ìý If you missed the session, youÌýcanÌýwatch it back on LinkedIn by following thisÌý.ÌýÌý

Have a great weekend! Hope you enjoyed this week’s round-up.    

ClickÌýhereÌýfor more by the author, or find us onÌý,ÌýÌýorÌýÌýfor the latest news. You can also read the last round upÌýhereÌýor keep an eye out for our next one!Ìý

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In this EDM Talk, we lift the lid on how our AI team is developing solutions to help our clients, especially in the area of entity matching and resolution. This plays an important role in on-boarding, KYC and obtaining a single customer view.

What is the the data challenge? 

Institutions such as banks, often have large sets of very messy data which may be siloed and subject to duplication. When onboarding a new client or building a legal entity master, institutions may need to match clients to both internal datasets and external sources. These include vendors such as Dun and Bradstreet and Bloomberg, or taking data from a local company registration authority, such as Companies House in the UK.  This data needs to be cleaned, normalised and matched to create a single golden record in order to verify their identify and adhere to regulatory compliance. For many institutions, this can be a heavily manual and time-consuming process.  

What needs to be done to improve entity matching? 

In entity resolution, there are two main challenges to address: the data matching side; and the manual remediation side which is required to resolve those instances where we have low confidence, mismatched or unmatched entities.  

Ä¢¹½¶ÌÊÓƵ undertook a recent Use Case where we explored matching entities between two open global entity datasets Refinitiv ID and Global LEI. We augmented our fuzzy matching rule-based approach with ML to address and improve efficiencies around the manual remediation of low confidence matches.  We performed matching of entities between these datasets using deterministic rules, as many firms do today. We followed the standard approach in place for many onboarding teams, whereby entity matches that are low confidence go into manual review. Within Ä¢¹½¶ÌÊÓƵ, data engineers were timed to measure the average time taken to remediate a low confidence match which could take up to one minute and a half per entity pair. This might be fine if there are just a few entities that you need to check but whenever you have hundreds, thousands or many hundreds of thousands this highlights how challenging the task becomes and the resource and time required to commit to this task.  

At Ä¢¹½¶ÌÊÓƵ we thought this was an interesting problem to explore. We were keen to fully understand whether AI-enabled data quality and matching would bring benefits in terms of efficeincy and improvement to data quality to our clients who undertake such tasks. 

What did Ä¢¹½¶ÌÊÓƵ want to achieve? 

We were particularly interested to understand how we could reduce manual effort and increase the accuracy of data matching. We wanted to understand what benefits machine learning would bring to the process, using an approach that was transparent and which would make decision-making open and obvious to an audit or regulator. 

What benefit is there from applying Machine Learning to this problem? 

Machine learning is a broad domain. It covers application areas from speech recognition, understanding language to automating processes and decision making. Machine learning approaches are built on mathematical algorithms and statistical models. The advantages of these approaches is the ability of the algorithms to learn from data, uncover patterns and then use this learning to make predictions on new unseen cases. We see machine learning deployed in everyday life from our email filters through to personal assistance devices such as Amazon Echo and Apple Siri. 

Within the financial sector, Machine Learning techniques are being applied to tasks including profiling behaviour for fraud detection; the use of natural language processing to extract information from unstructured text to enrich the Know Your Customer onboarding process; through to the use of chatbots to automatically address customer queries and customise product offerings.  

At Ģ¹½¶ÌÊÓƵ we view Machine Learning as a tool to automate manual tasks through to a decision making aid augmenting processing such as matching, error detection and data quality rule suggestion for our clients. This then frees up time and resource for clients enabling them to do more in their role.  

How can machine learning be applied to the process of matching? 

Within Ä¢¹½¶ÌÊÓƵ we have augmented our rules-based matching process with machine learning. Our solution has a focus on explainability and transparency to enable the tracing of why and how predictions have been made. This transparency is important to financial clients in terms of adhering to regulations through to the building of trust in the system which is providing these predictions. Using high confidence predictions, we can automate a large volume of manual review. For example, in the matching Use Case, we were able to reduce manual review burden by 45%, freeing up client’s time with expertise deployed to focus on the difficult edge cases. 

At Ä¢¹½¶ÌÊÓƵ we train machine learning models using examples of matches and non matches. Over time patterns within that data are detected and this learning can be used to make predictions on new unseen cases. A reviewer can validate the predictions and feed this back into the algorithm. This is known as human in the loop machine learning. Eventually the algorithm will become smarter in predictions making more accurate predictions. High quality predictions can lead to less manual review, by reducing the volume that need reviewed. 

The models we have built need good quality data. We used the Ä¢¹½¶ÌÊÓƵ self-service data quality platform to create good quality data sets and apply labels to that data.  Moving forward at Ä¢¹½¶ÌÊÓƵ, we are seeking to augment AI and to look at graph linkage analysis, as well as furthering enhancing our feature engineering and data set capabilities.  

To learn more about whatÌýthe work we are doing with machine learning and how we are applying it into theÌýÄ¢¹½¶ÌÊÓƵÌýplatform, all content is available on theÌýÄ¢¹½¶ÌÊÓƵÌýwebsite.ÌýWe also have aÌýwhitepaperÌýon AI-enabled data quality.Ìý

For a demo of the system in action please fill out the contact form. 

To find out more about what we do at Ä¢¹½¶ÌÊÓƵ, check out the full EDM talks video ! 

We will soon be publishing Part 2 of this blog series that will look at the application of AI and ML in the Fintech sector in more detail as well as an entity resolution use case.  

°ä±ô¾±³¦°ì here for the latest news from Ä¢¹½¶ÌÊÓƵ, or find us on ,  o°ù  

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