Article

06.03.2018

The banking profession through the prism of big data

In an article praising the smart use of data to redesign the banking model,Edouard d'Archimbaud of BNP Paribas says data is worth its weight in gold.

Fifteen years from now, will robots be able to exactly replicate a human brain? One thing seems certain: in 10 years, we will no longer be able to distinguish a biological voice from a robot's voice on the telephone, says Edouard d'Archimbaud, head of the artificial intelligence (AI) laboratory of the BNP Paribas Corporate and Institutional Banking Division. 

In years to come, the phenomenon is expected to have a major impact on the financial industry and create millions of opportunities, making it the greatest ever technological revolution. "Imagine, for instance, that we can extract a mountain of data from conversations between traders: despite the complexity, the machine would be able to pick up certain details of the job. Responses to requests for prices could be automated. And people and robots could collaborate to offer new services", says Edouard d'Archimbaud, one of the top 20 data scientists in global finance.

An algorithm can describe and comment on an image

Every day, 1.5 billion images are posted on Facebook. Today, almost all information is in digital format. This vast amount of interpretable data (pictures, descriptions, comments, etc.) is beginning to be readable by more and more sophisticated artificial intelligence systems. This means that the photos and captions we post are turning into a large educational tool for Facebook's robots.

Greater access everywhere 

Open source is working in favour of artificial intelligence, and the power of computers is skyrocketing. Data remains the key asset, however, dictating that some companies can be involved in AI, whereas others cannot.

Just as a child learns by observation, artificial intelligence learns by observing data. The more it receives, the more its performance is enhanced. "The bank, for example, mixes external data (such as transcripts of European Parliament speeches) with its own rich, free sources of internal data in order to create volumes of "trade" conversations that are not replicated anywhere else", says Edouard d'Archimbaud. This is how robots manage to master every nuance of our financial jargon."

How would banks be particularly affected?

Firstly because of their data "culture", in the sense that banking is an information processing industry. The statisticians of the past and today's data geeks have comparable profiles", d'Archimbaud continues.

Secondly, banks are especially affected because they invest an enormous amount in improving their operational efficiency. "We want to protect our customers' interests even better still, which is why we recently developed a tool for reviewing documents. One of our robots can cover 150 pages in 15 seconds. Another example is the tool that provides our internal and external translations: it enables us to manage information effectively in the many countries where the BNP Paribas Group operates. Robots have grasped some of the specifics of what we do, and the results are really exciting", he adds.

While robotisation enables greater quantities, AI, which is systematic and rigorous, enables improvements in quality. It excels at tasks which are difficult, repetitive and costly: it refines the accuracy of the analyses, to which human intelligence adds the finishing touch. AI is therefore transforming bank staff into "enhanced employees". It should be understood that AI adds to their capabilities. Without the assistance of robots, certain tasks simply could not be carried out by the required deadlines. The bank is also building new-generation "user interfaces" based on a range of components: webchat assistants, chatbots, search engines, and the ability to convert voice to text."

Altering the banker's value chain

The bank possesses a prized asset in its data – even if its use is obviously restricted by the bank's code of ethics. "In the same way as a diamond, the richness of which is derived from its multiple facets and complex structure, data releases its value when it is shaped and contextualised. Once combined, merged and cross-referenced, data can broaden the perspectives from which we "grasp" the customer's activity. New dimensions appear that once again create a "centricity" revolving around the customer. Once separate entities, data media such as e-mail and the internet now speak to each other!

"We no longer simply manage transactions; mostly, we draw out meaning from information that we blend together. It's a bit like the automotive industry: the core of the profession used to be manufacturing engines, but today, it has become a business that automates processes and information."

How can we accommodate this new phenomenon?

Do not be afraid that a machine will steal your job. But if your manager knows nothing about technology, it might be prudent to find employment elsewhere.

When was the last time you brought out a new product? Facebook changes its products all the time. And when did you last sign up a high-tech entity, or invest in a technology start-up?

If you have fears, confront them. Fall back on the fact that people and machines operate in tandem. Machines will learn effectively from us providing we always correct mistakes. And conversely, machines are able to pick up human imperfections and help us advance.

Robots work at high speed in a systematic manner free from constraints including tiredness, boredom, and limited time. They help us reassert the value of our roles, release our creativity and give more of ourselves to human relationships.

Article

22.10.2016

Digital transformation: your action plan

33% of IT decision-makers don't have a clearly defined strategy for digital transformation, a survey reveals. Why is this and how can this situation be rectified effectively?

In the fast-moving consumer goods market, technology is now radically reshaping competitive dynamics in the marketplace, for both consumers and distributors. This has irrevocably changed how people buy things. In the results of its "Fast-Moving Consumer Goods" survey, Progress reveals the current paradox: digital transformation is crucial, but implementation is slower than desired.

"60% [of IT decision-makers] admit that their organization is still largely in denial about the need to transform digitally."

Where exactly does the problem lie?

More than half of IT decision-makers see this process as something daunting that will take a long time. 66% feel that their marketing and IT teams are not in alignment to deliver on the project. 64% find it difficult to keep up with the ever-changing digital landscape. When it comes down to it, two departments clash on the distribution of tasks and budgets: IT versus Marketing. The good news? 96% of companies have plans to act within the year.

The key elements of your action plan

1. Begin with an inventory

Achieving digital transformation involves customer satisfaction, the ability to alter the focus of the business and rollout of a flexible platform, but you shouldn't throw out the baby with the bathwater. The transformation should be initiated after taking stock of the company's assets, in the form of an inventory.

Then you should visualize possibilities for change to consider the future of your business, no longer as a traditional firm, but as an ambitious digital hub.

2. Inspiration to think big

The founders of Google say that they have always sought to reject a traditional management approach by adopting two basic principles, which apply perfectly to digital transformation: focus on satisfying users and hire "smart creatives". This can be achieved through the LEAN method.

3. The will and the time

The company has to recognize that digital change is permanent in order to be relevant. It requires the creation of new experiences and the definition of new paradigms that can overthrow traditional models but are ultimately beneficial: 41% of CIOs notice an increase in market share post-transformation, with 37% of employees becoming more motivated as a corollary.

4. The vision and the tools

In order to improve and optimize the customer experience, you have to work on the speed, responsiveness, security and standardization of distribution channels. Mobile devices are widely favoured (62% according to the survey) for analytics, data connectivity, e-commerce, content and the Cloud.

A full copy of the report can be downloaded for free from the publisher's website.

Source: Progress

Article

18.11.2016

Big data: six questions to ask yourself before getting started

Big data is a new class of assets that companies must embrace, develop, protect and make work for them during their transformation into a digital enterprise. We have put together some points to help guide your strategy.

Is there a course in big data?

Most universities around the world have come to understand the importance of big data. More and more, they are using analysis, both in research and to improve the lives of students on campus and help guide them; however, there is little in terms of training on this topic. Nevertheless, some establishments have recently started to offer their own diplomas and programmes to train the next generation of data scientists.

Do I need to provide training for my staff?

Yes. However, it's difficult to send your IT teams back to the school room in order to train or bring them up to speed. Nevertheless, various training courses have been organised around the country by specialist service providers. A two-day training course already teaches its students about the specific issues surrounding big data and the potential technical solutions.

Do I need to hire a data scientist?

Not necessarily. Some figures: last year, there were 4.4 million jobs in this sector, of which only 40% were filled. Not everyone has the budget for a data scientist. You can instead call on an independent consultant to pave the way and get your company up and running with big data.

What main techniques are required?

Techniques such as machine learning and data mining are essential for those working with big data. They help you tackle tasks that are difficult or even impossible to complete using more classic algorithms. The art of Data Visualisation enables you to communicate discoveries from data analysis.

What keyword should I take away?

Hadoop! In the same way as Microsoft Office is known for productivity and Apache is synonymous with the internet, apps are the key in the world of Big Data. Hadoop should be the cornerstone of your strategy. Without such expertise, it is impossible to master big data. This open-source software framework is designed for distributed data storage. It is highly scalable and resistant to failures. Its role is to process and analyse new and old data silos to extract significant knowledge from them that can be used in a company's strategy. Your experts will have to become familiar with its components: ‘Spark’, ‘Hive’, ‘Pig’, ‘MapReduce’ and ‘HBase’.

Is big data relevant for SMEs?

Certainly, in particular for marketing: big data enables companies to sort data in order to gain a clear profile of its customers. Segmentation can be used to optimise campaigns. Analysis also allows you to  really observe how customers behave. SMEs don't have the same budget as a large group, and so they must primarily focus on data which is both crucial and can be exploited to reap the greatest reward: creating a stronger link with their customers.

Article

14.12.2016

Tant qu’il y aura des data…

Het ultieme doel van big data? Een unieke klantervaring creëren. Maar waarom lukt het een start-up zonder verleden om affectief te zijn, terwijl oudere bedrijven met tonnen data maar wat graag dichter bij hun klanten willen staan? Wat is de winnende formule?

Digitale gegevens opslaan en verwerken is niet nieuw. Datamining ook niet. Maar connected devices en mobiele toepassingen creëren letterlijk een tsunami aan gegevens. Sms'jes, chats, foto's, filmpjes, muzieklijsten, zoekopdrachten, clicks op het net, routeberekeningen op Google Maps en aanverwanten, onlinebetalingen, contacten met klanten via chatbots of e-mail, automatische bestellingen door slimme koelkasten ... We produceren de hele tijd gegevens zonder er ook maar even bij stil te staan! Ook als we akkoord gaan met geolocatie of wanneer we inloggen op een hotspot ...
Tegen 2020 zal het datavolume allicht vervijfvoudigen.  Zo genereert een geconnecteerde wagen in amper één uur miljoenen gegevens die niet alleen nuttig zijn voor de auto zélf, maar ook voor de verzekeraars en de e-commerce. Er staat veel op het spel: een strategie bijsturen, een service personaliseren, betere beslissingen nemen, tendensen opsporen, prognoses maken ...

Old-school statistici interpreteerden cijfers uit het verleden om voor een betere toekomst te zorgen. Hedendaagse  “data scientists' zijn geeks, er worden academische opleidingen georganiseerd en we zijn mentaal niet meer in staat om het explosieve groeitempo van de gegevens bij te houden. Alleen machines kunnen dergelijke gegevensstromen nog verwerken. Dankzij automatische leertechnieken ('machine learning') gebeurt dat beter en sneller. Er zou een standaard voor correct gebruik van artificiële intelligentie in de maak zijn op initiatief van kleppers als Google, Facebook, Amazon, IBM en Microsoft. Volgens Nicolas Méric, oprichter en CEO van de start-up DreamQuark, gespecialiseerd in deep learning in de gezondheids- en verzekeringssector, verhogen dergelijke technologieën de menselijke capaciteiten, maar zijn ze niet bedoeld om volledig autonoom  te werken.

Wie komt in aanmerking?

Geen enkele sector ontsnapt aan de behoefte om gegevens te verzamelen en die te gebruiken om zijn omgeving nuttig aan te passen. Maar de ene is al gehaaster – of opportunistischer– dan de andere. Telecommunicatie, transport, gas-, water- en elektriciteitsleveranciers nemen het voortouw: de Franse spoorwegen maar ook schoonheidsproductenfabrikant Nuxe speuren alle onlinekanalen af op zoek naar klantencommentaren om hun klanten beter te leren kennen. Liftenfabrikant ThyssenKrupp wil zijn liften en vooral de gebruikers ervan optimaal bedienen en verzamelt allerhande liftparameters om het onderhoud te optimaliseren en vervelende storingen te voorkomen.
Big-data-managers in bedrijven staan voor drie grote uitdagingen, ook wel bekend als de '3V's': grote Volumes verwerken, rekening houden met de oneindige Variatie van gegevens en omgaan met de Velocity of snelheid waarmee ze worden gegenereerd.
Banken ontsnappen daar niet aan. Ze hebben er zelfs enorm veel bij te winnen, want ze beschikken over tonnen transactionele informatie over hun klanten en creëren allerhande processen. Zij staan dus voor de uitdaging om zelf ook met die schat aan gegevens aan de slag te gaan en binnen een zo kort mogelijk tijdsbestek nieuwe diensten met toegevoegde waarde uit te testen.

Momentum

Jean-François Vanderschrick is Head of Marketing Analytics & Research bij BNP Paribas Fortis: "Wat mij fascineert is niet zozeer de hoeveelheid beschikbare gegevens en connected devices, als wel wat de technologie er tegenwoordig allemaal uithaalt. Er gaat geen dag voorbij zonder dat ik van iets nieuws opkijk. JP Morgan komt tendensen op het spoor door foto's te kopen van de bezetting van supermarktparkings. China ontwikkelt gelaatsherkenning om de lay-out van zijn interfaces aan te passen aan de gelaatsuitdrukking van zijn klanten. Sokken 'made in USA' kunnen we volgen zodra ze verzonden worden totdat we ze in huis hebben ... Het is allemaal onderdeel van ons dagelijkse leven. Net zoals een bank die zich wil gaan aanpassen aan de levensfase waarin haar klant – die ze volgt sinds hij een rekening heeft – zich bevindt om hem precies datgene aan te bieden wat nuttig voor hem is."

BNP Paribas Fortis zette onlangs een nieuwe stap in de dataverwerking met de benoeming van een Chief Data Officer die lid is van het uitvoerend comité, Jo Couture. Dat betekent ook dat er meer mankracht, nieuwe tools en nieuwe capaciteiten onderweg zijn.

Jean-François Vanderschrick: "Data analytics moet ons in staat stellen om de klantervaring te verbeteren en de kosten onder controle te houden. Meestal gaat dat samen met een verhoogde efficiency."

Volgens hem belandt de leercurve nu pas in de exponentiële fase terecht.  

De timing is even belangrijk als de service zelf

Data komen in heel wat domeinen van pas: operationele excellentie, marketing, fraudedetectie, kredietrisico ... De bedrijven hebben intussen ingezien dat ze hun gegevens in kennis en diensten moeten omzetten en hebben vaak alles in huis om dat goed te doen, op voorwaarde dat ze zich niet door de oceaan van gegevens laten overspoelen. Het moeilijkste – en een bron van frustratie – is wellicht het ontsluiten en kwalificeren van de gegevens. Compliance-aspecten hebben de natuurlijke neiging om de ontwikkeling af te remmen, terwijl een kortere data -to -market juist heel belangrijk is. Dikwijls laat de marktintroductie te lang op zich wachten. Verder is het van belang om een service in real time aan te bieden. Supermarktketen Monoprix analyseert bijvoorbeeld het verwerkingsproces van de 200.000 dagelijkse bestellingen van zijn 800 winkels om onmiddellijk te kunnen ingrijpen op de supply chain. Voor de Franse winkelketen is dat een kritiek proces.

"Er moet een juiste dosering worden gevonden tussen tests (het prototype van de service oogt dikwijls bijzonder fraai, maar het veralgemenen ervan lukt niet altijd), risicometing en prioritering van de doelstellingen", zegt Jean-François Vanderschrick.

Het algoritme 'opvoeden'

Als je over de gegevens en de technologie beschikt en er financiële belangen meespelen, staat er geen grens op het ontsluiten van de gegevenswaarde. Je verbeelding is de enige beperking. Naast grote en complexe projecten zijn ook hier vrij eenvoudige quick wins mogelijk en wenselijk. Zo kunnen de operationele directies van de onderneming elementaire analyses verrichten op grote gegevensvolumes.

"Er zijn heel wat soorten gegevens die er misschien volstrekt onbelangrijk uitzien, maar die toch informatie verschaffen en tot actie aanzetten: een klant die met de concurrentie werkt, die elders kredietlijnen opent of een zeer groot bedrag leent, die met een ander land werkt ... Al die gegevens verdienen onze aandacht vanuit commercieel oogpunt,  in 70 procent van de gevallen zijn ze relevant", voegt de BNP Paribas Fortis manager eraan toe. Door het transactionele model van een klant te onderzoeken kunnen we betere kredietbeslissingen nemen. Relevante beslissingen liggen meer voor de hand met een model dan zonderweet Jean-François Vanderschrick. "Via machine learning leren we het algoritme om antwoorden te geven die meer en meer to the point zijn", voegt hij eraan toe.

'Big is better', ook voor kleine ondernemingen?

Dankzij de cloud hebben kmo's vandaag de nodige opslag – en rekencapaciteit – om de gegevens te verwerken. Bedrijfsmanagementprogramma's die gebruikmaken van de cloud-technologie, zoals CRM, tools om bestellingen of productiekosten of de traceerbaarheid van leveranciers te volgen, maken big data toegankelijk voor kleine en middelgrote ondernemingen. Op één voorwaarde: dat alle gegevens op dezelfde plaats samengebracht worden. Het verschil tussen corporates en kmo's speelt zich af op lange termijn. Maar kmo's voor wie eenstatisticus te duur is, kunnen altijd specifieke studies kopen en hun gegevens uitbreiden met externe databases ...

(Bronnen: BNP Paribas Fortis, Les Echos, Transparency Market Research, IDC, Ernst & Young, CXP, Data Business)

 

 

Article

27.12.2016

These 4 giants from Silicon Valley want to seduce your IT management

Already champions in everyday life, Google, Facebook, Slack and LinkedIn are adopting innovative and complementary approaches to convert companies. What strategies are they implementing in order to convince you?

Google: the value of data intelligence

Google is adopting an approach which goes beyond communication tools and suites of productivity apps/services. The company has largely transformed its business divisions so that they can exploit cloud infrastructures, big data, analytics and machine learning as a matter of priority. Two competitors are blocking it along the way: Amazon and Microsoft, but for different reasons. Developers have been using Amazon Web Services for a long time, which gives it a history of trust. Microsoft (Cloud, Office) also has a historical presence in IT departments around the world. In this approach, linked to the processing of sensitive data, Google still needs to evangelise: a company is not as easily convinced as a consumer, particularly when it comes to strategic or confidential data. Its weapon: the power of its artificial intelligence tools to process data silos.

Facebook: introducing WorkPlace, naturally

After more than a year of development with partner companies such as Danone, Starbucks, Royal Bank of Scotland and Booking.com, Facebook officially launched WorkPlace last October. This Facebook spin-off enables organisations to create an internal social network - completely private and secure - within an interface familiar to all employees in their everyday life, introducing head-on competition for already widespread tools such as Chatter (Salesforce) or Yammer (Microsoft). Unlike free Facebook, WorkPlace is billed monthly depending on the number of users: $3 for the first 1,000, $2 for the next 9,000 $1 for over 10,000 users.

Slack: real-time collaboration becomes mainstream

Despite the introduction of Microsoft Teams on its turf, Slack remains confident in its strategy of creating tools that allow greater communication and productivity within companies.

"We find this offensive both flattering as well as intimidating, given Microsoft's means, but we think there is sufficient space in the market for several players", declared April Underwood, VP of Slack at the beginning of November.

A market that Slack has largely contributed to opening and driving, by introducing the concept of real-time collaboration. Its weapon? Agility, despite its still limited size and its proven and copied tools. Result: 4 million active users everyday and constant growth.

LinkedIn: from B2B marketing for... Microsoft

Microsoft Closes Acquisition of LinkedIn at the beginning of December. The transaction, which runs into billions of euros, has been followed closely by the European Commission. Despite a strong position in the business, mainly at a human resources level, LinkedIn needs 25 billion euros from Microsoft to pursue its offensive in the domain of professional tools, in a hugely competitive climate. For Microsoft, the acquisition will enable the company to reach B2B marketing targets such as recruitment agencies, head-hunters and businesses. To explain the synergy sought in simple terms, the CEO of Microsoft, Satya Nadella, gives the example of a meeting where everyone present sees their LinkedIn profile, linked to their invitation.

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