Welcoming our two new apprentices

We kicked off the week by announcing the exciting news that two new apprentices from the Belfast Met have joined our DevOps team. The firm has now grown by 25% since March 2020 and approaching 130% over the past two years, in response to rapidly growing customer demand. 

The new recruits, Natalia Walsh and Victoria Wallace, will balance their four days a week at Datactics with one day spent studying for the Level 3 “Networking Infrastructure with Cyber Security” course at Belfast Met. 

If you want to read more about the new apprentices joining, check out our press release here. 

We want to take the opportunity again to welcome them both to our team! 

Awards and Events at Datactics! 

As the week continued, we unpacked the various awards we have achieved such as the Women in Diversity Award and the Investors in People accreditation. Highlighting these awards allowed us to express our progressive vision to further embed diversity within the team and invest in our people accordingly in a diverse and inclusive manner. 

We also released three look-back blogs which reflected on three recent notable speaking engagements for the company.  

The first of which was Fiona Browne’s contribution to FinTech Finance. The interview covered the extent of the Anti-Money Laundering (AML) fines currently faced by banks over the last number of years and began to unpack what we do at Datactics in relation to this topic. 

In the blog we looked in detail into the following key questions that were put to Fiona in the FinTech Finance session:  

• How can banks arm themselves against increasing regulatory and technological complexity? 
• Where does Datactics fit in to the AML arena? 
• Why should banks look to partner, rather than building it in house?  

Data will power the next phase of our economy

We then reflected on Kieran Seaward’s DMS Virtual Keynote, ‘A Data Driven Restart’, unpacking the key themes and questions regarding the challenges presented by COVID-19, including a wide range of changes to the way business can be conducted.  

At Datactics we have been really encouraged that engagement with the market is still strong; since March, and the start of many lockdowns, we’ve conducted many hundreds of calls and meetings with clients and prospects to discuss their data management and business plans. The blog is based on our key findings from these calls and reflects the priorities many data-driven firms have. 

The key questions addressed in the blog are as follows: 

What is the importance of a foundation of good data quality? 

What comes first? Data Governance or Data Quality? 

Is it necessary to get data quality under control?

We then rounded off the week by looking back to Stuart Harvey’s contribution to the Belfast International Homecoming 2020. The panel itself was chaired by Jayne Brady, Digital Innovation Commissioner and aimed to discuss the question: Can Belfast’s Technology Companies Lead an Inclusive Recovery?   

The key themes that were delved into by Stuart and the panellists included: 

• The vast changes in the way organisations are approaching work 
• Diversity being key to the development of technology
• The vitality of not simply the ‘right’ education but education 

FinTech Festival is coming soon! 

In other news, Matt Flenley and Jordan Wray are looking forward to the upcoming Singapore FinTech Festival as guests of Invest NI. This week there was a late-night networking event on Wednesday which made for some great virtual chats and introductions. It has got us ready for the FinTech Festival which is coming up soon! 

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

Click here for more by the author, or find us on LinkedIn, Twitter, or Facebook for the latest news. You can also read the last round up here or keep an eye out for our next one! 

The post Two new apprentices have joined our team and we are gearing up for FinTech Festival! – Datactics weekly round-up appeared first on Datactics.

Datactics Head of Artificial Intelligence, Dr. Fiona Browne, recently contributed to the episode of FinTech Finance: Virtual Arena. Steered by Douglas MacKenzie, the interview covered the extent of the Anti-Money Laundering (AML) fines currently faced by banks over the last number of years and start to unpack what we do at Datactics in relation to this topic: helping banks address their data quality, with essential solutions designed to combat fraudsters and money launderers.  

How can banks arm themselves against increasing regulatory and technological complexity?

Fiona began by highlighting how Financial Institutions face significant challenges when managing their data. However, the increase in financial regulations since the financial crisis of 2008/2009, ensuring data quality has gained in its importance, obliging institutions to have a handle on their data and make sure it is up to date. Modern data quality platforms mean that the timeliness of data can now be checked via a ‘pulse check’ to ensure that it can be used in further downstream processes and that it meets regulations.

Where does Datactics fit in to the AML arena? 

A financial institution needs to be able to verify the client that they are working with when going through the AML checks. The AML process itself is vast but at Datactics, we focus on the area of profiling data quality and matching – it is our bread and butter. Fiona stressed the importance of internal checks as well as public entity data, such as sanction and watch lists.

In a nutshell, there is a significant amount of data to check and compare and with lack ofquality data, it becomes a difficult and costly task to perform so we at Datactics, focus on data quality cleansing and matching at scale.

Why should banks look to partner, rather than building it in house? 

One of the key issues of doing this in house is not having the necessary resources to perform the required checks and adhere to the different processes in the AML pipeline. According to the Financial Conduct Authority (FCA), in-house checks and a lack of data are causing leading financial institutions to receive hefty fines. Fiona reiterated that when Banks bring it back to the fundamentals and get their processes right and data into order, they can then use the partner’s technology to automate and streamline these processes, which in turn speeds up the onboarding process and ensure the legislation is being met.

Why did the period of 2018/2019 have such a high number of AML breaches?

Fiona explained that many transactions go back over a decade, it takes time to identify such transactions. AML compliance is difficult to achieve and regulators know that it is challenging. The regulators are doing a better job at providing guidelines to financial institutions, enabling them to address these regulations. Fiona reaffirmed that perhaps 2018/2019 was a wakeup call that was well needed to address this issue. 

And with AML fines already at $5.6 billion this year, more than the whole of 2019, what can banks do? 

Looking at the US, where although the fines for non-compliant AML processes are not as high as 2019, there is still a substantial number of fines being issued, Fiona said that it is paramount to ensure financial institutions have the right data and the right processes in place. Although it can be considered as an administrative burden, there is real criminal activity behind the scenes, which is why AML is so important. It is vital that financial institutions get a handle on this, enabling them to also improve the experience for their clients. 

The fines will continue to be issued. Why should firms look to clean data when they just want to get to the bottom line? 

It is essential to have the building blocks in place. Data quality is key for the onboarding process, but it is also essential downstream, particularly if you are wanting to do more trend analysis. Getting the fundamentals right at the start will pay back in dividends.  

Are there any other influences that Artificial Intelligence (AI) and Machine Learning (ML) can have on the banks onboarding process? 

According to Fiona, there is no silver bullet. One AI/ML technique will not solve all the AML issues. It is about deploying these techniques when approaching the issues in different ways. A large part of the onboarding process is gathering data and extracting relevant information from the data set. Fiona has seen a lot of Neuro-Linguistic Programming (NLP) techniques employed to extract the data from documents. At Datactics, we use Machine Learning in the data matching process to reduce the manual review time. ML techniques are employed in supervised and unsupervised approaches geared to pinpoint fraudulent transactions. We think that the graph databases and network analysis side of machine learning is an interesting area, we are currently exploring how it can be deployed into AML and fraud detection. 

Bonus content: In the US and Canada, one way to potentially identity fraud was to look at transactions that were over $10,000. The criminals however become increasingly savvy and utilise Machine Learning to muddy their tracks. By doing this, they can divide transactions into randomised amounts to make them appear less pertinent. As Fiona put it ‘the cat and mouse game’. 

If you are employed in the banking sector or if you must deal with large and messy datasets, you will probably face challenges derived from poor data quality, standardization, and siloed information. 

Datactics provides the tools to tackle these issues with minimum IT overhead, in a powerful and agile way. Get in touch with the self-service data quality experts today to find out how we can help.

The post How can banks arm themselves against increasing regulatory and technological complexity? – FinTech Finance appeared first on Datactics.

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.  

Datactics 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 Datactics, 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 Datactics 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 Datactics 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 Datactics 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 Datactics 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 Datactics 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 Datactics self-service data quality platform to create good quality data sets and apply labels to that data.  Moving forward at Datactics, 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 what the work we are doing with machine learning and how we are applying it into the Datactics platform, all content is available on the Datactics website. We also have a whitepaper on AI-enabled data quality. 

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

To find out more about what we do at Datactics, check out the full EDM talks video below! 

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.  

Click here for the latest news from Datactics, or find us on Linkedin, Twitter or Facebook 

The post EDM Talks: Lifting the lid on the problems that Datactics solves appeared first on Datactics.