05 Mar

Watch our presentation from Energized Labs: Machine Learning with Human-in-the-Loop

André Louçã presents a thought provoking talk in this Energized Labs video, detailing how the path of Warwick Analytics and Machine Learning have changed and developed over time.

Watch the video now to hear André explain the main technology developed by his team, and a demonstration of Predicx, showing that there is no need to have huge teams of labellers when only one person is able to maintain the model providing trustable output.

 

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23 Feb

Warwick Analytics featured in Global Predictive Maintenance Market Research Report

The report analyzes competition and latest developments on the future Predictive Maintenance market. On the basis of major manufacturers, the global Predictive Maintenance market is segmented based on the key manufacturers, growth rate, Predictive Maintenance revenue, research and modification taking place. In addition, it gives the rise in opportunities for companies in the Predictive Maintenance market. Some of the outstanding manufacturers in the Predictive Maintenance market enclosed in the report are Warwick Analytics (PrediCX), SKF, PTC, Robert Bosch, SAP SE, IBM, General Electric, Rockwell Automation, Software AG and RapidMiner. The report also includes a detailed analysis of Predictive Maintenance key market segments and sub-segments.

From a geographical prospect, the report studies the Predictive Maintenance market across regions such as North America, Europe, Latin America, Asia Pacific, Middle East and Africa. The regional market will get advantage from the well-established Predictive Maintenance framework and the high level of digitizing in the region’s Predictive Maintenance sector.

You can get a sample copy of the report here.

 

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09 Feb

Digital Transformation or just better FAQs?

A lot has been made of digital transformation, and how many businesses are using self-serve web-based applications to engage with their customers, employees and other stakeholders to be able to enhance and in some cases reinvent the customer experience often with both a stickier customer journey and lower service costs. Uber and AirBnB are often held up as the poster-boys but there are many businesses who are not ‘digital native’ companies emulating in their own way.

As with so many buzzphrases, there is usually a less-sexy way of saying the same thing which has been around for a long-time. In the field of customer interaction, most people will think of digital transformation as the growth in chatbots and social media-enabled communication. However I would argue that the main bastion of change has to be directed at FAQs.

Sexy or not, FAQs used to be the only way to read about self-help and avoid calling a contact center. They are frequently cited as inherently flawed as these blogs from the UK Government and eloquently in this technical writers blog. Yet if you stop and think, a well-structured FAQ if it is searchable with natural language is a critical asset as it is really the same thing as a chatbot, but perhaps without the charm or manners.

In a more measured manner, really FAQs are part of a spectrum of communication channels (one-way and two) where customers can solve problems. They sit alongside forums, social media, chatbots, chat, phone and email (see diagram below).

Also surveys and reviews can trigger interactions depending on the content. The current state of most organisations is that all these elements are separate silos, and whilst the customer experience team are trying hard to break these silos down, there are few who would see FAQs as on the same spectrum as chatbots and forums. Also people’s expectations of FAQs are to see a laundry list of requests which is not how they desire to interact. Imagine though if you could write your query in any way you wanted into a search bar and it would retrieve the correct response. Imagine also that the search was entirely consistent across all channels. Is this just a fantasy?

Machine learning for text is capable to classify interactions to be able to automate responses to natural language. However as we see with chatbot fails, it is hard to get this right due to the complexity and variability of human dialogue and chatbot containment rates are still below where their proprietors would want them to be. Further complexity is that human dialogue varies immensely across the channels: People don’t write in emails how they use chat which is different again to forums, nor how they speak or write a complaint, or even fill in a survey. By way of an example, a study at an airline found that the average topic in a chat was just over one whereas in a call it was nearer to two and in a complaint it was two and a half. People use different channels for different things, and also use different channels for the same thing in different ways. If the company is trying to classify aka tag or ‘label’ each interaction, then it will very easily fall into the trap of having different categories or tags for different channels, not by design but because it is hard to normalise them whatever technology you’re using. This phenomenon doesn’t really have a formal name but it is rife and disruptive. The ideal is some kind of ‘homogenization’ of the tags i.e. so that “late shipping” can be the same concept whatever channel. This then allows the guardians of the customer journey to understand what’s going wrong (and right), get a global view, and also understand if customers are calling back about the same thing on a different channel because they didn’t get it resolved. This also means that the customer journey and knowledge base can be fixed once for each breach, in the knowledge that it is fixing things across the board.

Machine learning can help this harmonization process although it is fraught with challenges, not least because the models for each tag need to be built especially for each channel, for example the “late shipping” tag for chat will need to be a different model to the “late shipping” tag for email or complaint. What data scientists know is that the process of building the machine learning models is intense: New York Times estimated that up to 80% of a data scientist’s time is spent “data wrangling”. CrowdFlower estimates “data preparation” at 80%. Further, assumptions and errors are an inevitable part of the process where human judgement and skill is required. More than this, 76% of data scientists view data preparation as the least enjoyable part of their work. Furthermore someone needs to build a training set for the models and that typically involves a human somewhere labelling the various interactions into topics and a topology that can drive the correct response. This is laborious in a linear fashion.

There are a number of different approaches to this problem. One company addressing this problem in a novel way is Warwick Analytics which is a spin-out from The University of Warwick. It has developed a proprietary technology called ‘Optimized Learning’ which puts a ‘human-in-the-loop’ in a very effective way: What this means is that the technology classifies the customer interactions in a meaningful way but when its certainty is low, it asks for assistance for a human to classify or ‘label’ the interactions which provide the most information back to training the models. Therefore it is theoretically and practically guaranteed to involve the minimum human interaction to maximise the performance of the models and hence the accuracy. The human trainer can be offline, as well as involving the customer in certain circumstances. The company has worked with many enterprises to improve chatbots, automate contact centers, complaints handling and improve the quality of self-service and FAQs.

So in conclusion, FAQs are an old-fashioned and much discredited digital experience, yet in the new world of digital transformation and harmonization, they can come back to center stage thanks to some clever technology and the human-in-the-loop.

 

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08 Feb

Warwick Analytics in the Press: Data Science Central, Data Scientists Need Designer Labels Too

Warwick Analytics has been featured on Data Science Central discussing how ‘Data Scientists need Designer Labels Too’.

 

Overview

When we want to understand what people believe or perceive, we do it by analysing their communication either written or spoken. Let’s say we’re wanting to analyse voice of customer text data.

The classical way to approach this is text mining based on keywords and rules to drive topic analysis e.g. using TFIDF or some other kind of ‘vectorization’, and sentiment analysis of the opinion terms.

Thankfully this no longer needs to be the case thanks to the latest technologies. Imagine a world where AI-based labelling and machine learning for text is cheap and plentiful, where data scientists are not required to tune and drive models.

There are issues here. Firstly, what are we supposed to do with all the topics? If we build a word cloud how useful is that? If they use synonyms which aren’t in a dictionary, do we group these together in advance? We are essentially trying to second-guess and group terms, which might not match the intentions of the customers, or be different for different situations. Things for sentiment analysis are even more dissonant and we haven’t begun to explore the technical challenges with sarcasm, context, comparators and double negatives which all perform very poorly in such analyses.

So how else are we meant to analyse text data, apart from painfully compiling dictionaries and constant manual checking? Well, say hello to the wonderful world of labels. The labels being referred here are generated from machine learning i.e. by replicating human judgment based on a training sample of manually labelled data. The machine doesn’t need to be told keywords, it figures out common patterns which might be a lot more than single keywords, and might include where they are in the sentence and whether they are nouns or verbs, just as a human might.

You can read the full article here

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08 Jan

Warwick Analytics shortlisted for Accenture’s FinTech Innovation Lab London to Develop Solutions for Wave of Regulatory Challenges in 2018

Warwick Analytics has been announced as one of 20 global RegTechs, start-ups offering technology solutions for financial firms’ regulatory challenges, that will join Accenture’s sixth FinTech Innovation Lab London.

During the three-month fintech accelerator programme, which runs Jan. 2 – Mar. 22, Warwick Analytics and other fintech start-ups will be partnered with executives from banks and insurers to fine-tune and develop their technologies and business models.

Accenture launched the RegTech stream in response to an increased pool of start-ups offering solutions for compliance in a year in which the financial services industry faces unprecedented levels of regulation. Among the new regulations this year are the revised Payments Services Directive (PSD2), which requires banks to make customer data available to third parties, with the customer’s consent; the General Data Protection Regulation (GDPR); and the Markets in Financial Instruments Directive (MiFID II), which went into effect last week – all before structural banking reforms, with ringfencing, are implemented in January 2019.

The 20 companies on this year’s shortlist of innovative startups come from the U.K., Israel, Croatia and South Korea, offering technology solutions for many pressing business issues, including:

  • aiding and automating compliance processes, using analytics and robotics;
  • detecting financial crime and fraud, using artificial intelligence;
  • helping insurers predict and price cyber risk;
  • ensuring the security of payments and digital identity through blockchain solutions; and
  • developing financial management platforms across connected devices.

“The risk of non-compliance is what keeps financial boards awake at night,” said Julian Skan, executive sponsor of Accenture’s Fintech Innovation Lab London. “As the drive for better customer experience and lower unit costs pushes data into the cloud, the price of getting things wrong has risen. It’s a pivotal moment for technology solutions to help banks and insurers not just to meet the needs of regulators, but make the most of the digital economy.

“Above all, financial firms know they need to improve their productivity, particularly in the UK economy, and innovations can be the lightbulb moment for banks and insurers to operate more effectively and deliver better results. This year’s cohorts have shown how start-ups are learning to focus on problems that can be solved and to understand what they need to learn from incumbents who are facing the challenges of meeting digital customer expectations with legacy infrastructure.”

More than 270 start-ups from 42 countries applied to this year’s program, with the shortlisted start-ups being mentored by the program’s biggest-ever cohort of financial services executives.

Partners come from over 32 financial institutions including: AIB, AXA, BAML, Citi, Credit Suisse, Direct Line, DNB, Ergo, Goldman Sachs, HSBC, Intesa Sanpaolo, JPMC, Legal and General, Lloyds Banking Group, LV=, Morgan Stanley, MS Amlin, Nationwide, Nordea, OP, Post Office Management Service, RBS, RSA, Santander, Societe Generale, Towergate, TSB, UBI, UBS, XL Catlin, Zurich.

Dan Zinkin, a managing director at JP Morgan Chase, said, “Financial firms have an important role in collaborating with start-ups to develop new technologies that can transform our industry. We must keep ahead of a rapidly changing world and keep striving to innovate for our customers and improving our services. I am thrilled to be a part of a program dedicated to bringing financial firms and entrepreneurs together to navigate the future of the industry.”

Eight of the 20 shortlisted startups will go on to present to venture capitalists and financial-industry executives at the program’s Graduation Day on March 22.

Accenture and a dozen major banks launched the FinTech Innovation Lab London in 2012, with support from the city’s mayor and other government bodies. Since its launch, 56 start-ups have participated in the London Lab, securing more than 50 contracts with global banks and creating more than 800 jobs.

The London Lab is modelled on a similar program that Accenture co-founded in 2010 with the Partnership Fund for New York City, the US$150 million investment arm of the Partnership for New York City. In 2014, Accenture launched FinTech Innovation Labs in Asia-Pacific and Dublin. Globally, the Labs’ alumni companies have raised more than US$863 million in financing after participating in the program.

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