Data Blog

A blog about all things data: data analytics, data management, data governance, data quality, data visualisation. Gain insights from our data professionals working across all industries.

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29 November 2017

Data and analytics: Protecting us against financial crime part 1

By Jeremy Davey and Scott Samme The Chicago mobster Al Capone is famously (and perhaps erroneously) credited as the source of the phrase ‘money laundering’ in the 1940s, after his laundromat businesses were used to legitimise cash he gained through less salubrious means. Even Capone might have been surprised at how the money laundering business has boomed since his days. Our Global Economic Crime Survey shows the scale of the problem: global money laundering transactions worldwide are estimated at between $1 and $2 trillion – that’s 2% to 5% of the world’s total GDP. Less than 1% of this money is ever recovered by the authorities.

16 November 2017

The Gender Big Data Gap

By Harjinder Kaur - PwC, Navin Haram - UN Women and Fiona Bayat-Renoux - UN Women “To achieve gender equality and empower all women and girls” is the fifth of the 17 Sustainable Development Goals (SDGs) set out in the United Nation’s plan, Transforming our world: the 2030 Agenda for Sustainable Development. In our previous blogs we’ve discussed how big data can be used to monitor and evaluate international development. Big data can give us great insights into aid effectiveness and development projects, but it also comes with its own challenges.

28 September 2017

Big data, big deal?

By Maaike Platenburg, Freddy Bob-Jones and Friso Wiegman Benefits and challenges associated with Big Data for results measurements In our last blog, we explored the use of big data for monitoring and evaluation (M&E) purposes in international development. We concluded that big data can provide us with great insights into aid effectiveness; it can show whether development projects have been successful and facilitate a more adaptive process toward designing programmes that deliver better outcomes for disadvantaged people. However, current projects are still in the pilot phase and much still remains to mainstream the use of these techniques. With this blog we would like to build on our first blog by exploring the potential benefits and practical challenges associated with applying big data.

15 August 2017

Deploying Machine Learning in claims reserving

By Paul Delbridge Many insurers are investigating how Machine Learning can be best deployed to both improve risk segmentation and enhance pricing models. Many insurers have already acknowledged the speed at which both supervised or unsupervised Machine Learning can be used to build new types of high-quality models, leverage Big Data, and identify new relationships between variables. Relatively little has been made to date of the capabilities of Machine Learning or Artificial Intelligence to dramatically increase both the speed at which claims reserving can be undertaken, and the extent to which highly sophisticated automation can be introduced.

31 July 2017

Simplifying Fraud Analytics

By Neil Houston ‘Big Data’. Personally, I view the term as a bit of a misnomer, it is after all just data. Though I’ll grant that there it is more of it being created, and more importantly, organisations are holding on to it for longer. There appears to be a view in some organisations that the more data you have access to then the better chance you will have at deriving value or insight from it. Over the last 10 years I’ve seen that it is becoming more challenging for those responsible for detecting or investigating fraud

17 May 2017

Overwhelmed by complex data?

By Jonathan Watters Businesses have never had more access to data – and that brings enormous problems as well as benefits. The more data you have, the more difficult it can be to organise. Fortunately, there’s technology available to help. Managing big, data-heavy projects can be daunting. To give just two examples, we’ve worked recently with a company that needed to review its data for thousands of counterparties for compliance with anti-money laundering legislation. The company was given six months to complete the review, which would mean processing, checking and (if necessary) remediating 5,000 case files, each one of which had many supporting documents. In another, we worked with a national government that needed to review more than 1,000 capital projects and decide on a case-by-case basis

26 April 2017

Reviewed your bank’s chat metadata lately? Maybe you should – before the whistle blows

By Amjid Mahmood Imagine this scenario. You’re the head of compliance at a leading bank, and an employee who works on the trading floor comes forward with some very serious allegations about a senior trader. According to this whistleblower, the trader has been disclosing confidential information about clients to external parties and sharing information on their trading positions. You know you have an obligation to conduct a thorough internal investigation and report any significant findings to the regulator. But you also know that the sheer volume and diversity of data that you’ll need to comb through will present challenges if you use in-house resources to do it.

19 April 2017

Deploying machine learning in insurance pricing

By Paul Delbridge Insurers have been investigating the deployment of machine learning techniques in the pricing arena, seeking to exploit the speed at which models can be built and refreshed compared to the use of more traditional generalised linear modelling techniques. Machine learning techniques offer significant advantages over these traditional models, including the availability of various types of non-linear models which can lead to a wide range of new insights. However, these new models are more difficult to explain to both brokers and to customers (especially when these more sophisticated models suggest significant changes compared to the expiring prices), and there is a degree of resistance to what might be viewed as a black box technology by management and marketing teams.

10 April 2017

What can your phone say about you?

By Kavitha Chandrasekaran Our phones have become an essential part of our lives – and that means that every day, we leave a data trail behind us. From emails and social media messages, to pictures and videos, we leave a digital footprint wherever we go. Mostly, this is entirely intentional; it’s rare to find someone who owns a phone that doesn’t have any saved messages and images. But when something goes wrong, information is everything – which is why, in the world of forensic investigations, mobile data can be a valuable goldmine. ‘Mobile forensics’ is becoming an important part of the work of our Forensics Data and Analytics teams – we are increasingly asked to collect and analyse mobile data during investigations. It’s possible to collect an amazing array of information from the average smartphone, from SMS messages and emails, to calendar events, internet history and bookmarks and even your social media history. It’s even possible to recreate deleted messages, social media, pictures and documents from a phone.

04 April 2017

How businesses are transforming revenue models by monetizing and protecting customer data

By Paul Blase Information has become a primary form of capital - to businesses that know how to monetize their data, that is. Organisations that embrace the art of the possible in data monetization stand to create new capital from data sources and ultimately transform their business models. In doing so, however, they must maintain a sharp focus on data privacy. Capturing and monetizing data will require intelligent, connected systems and processes that help ensure that data and privacy are managed with the same rigour as traditional assets. The good news is that, as the volume of data has multiplied, the technologies for capturing, analysing and storing information have become more powerful - and less costly.

10 February 2017

Part II Data Governance banana skins: Enterprise data governance – the final step must be the first

If you read the first part of this series you’ll undoubtedly have been on the lookout for Data Governance banana skins and with any luck, will have avoided a few slips! In this blog I’ll talk about the silver bullet that will (apparently) resolve all data-related issues in one go – the Enterprise-wide Data Governance initiative. This subject deserves all the airtime it can get – it’s where most programs fail before they even begin.

02 February 2017

Using Personal Data to Build Customer Trust and Competitive Advantage

Every time you use a search engine, land on a website, buy a product or download an app, you generate and share personal data via a sophisticated and sometimes covert combination of tracking tools like cookies, beacons and e-tags. This expansion in personal data collection is creating enormous visibility into individual lives, preferences and behaviors, a trend that will escalate as technology continues to evolve with breakthroughs such as wearable devices, autonomous automobiles and the Internet of Things. The proliferation of data also represents a potential treasure trove of opportunity for companies seeking new sources of revenue and competitive advantages. In fact, 64% of respondents to PwC’s 20th Annual Global CEO Survey

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