What’s currently trending and will soon be the must-haves for any merchant? Without a doubt, the latest technologies that can enhance businesses, ultimately helping them scale.
Back to basics: How can data science add value to your business?
Keeping up with modern consumers requires you as a merchant to be up to date in terms of the customer journey they provide. In order to do that, they make use of data science – crunching the numbers and extracting valuable consumer journey knowledge that they can later translate into business strategies. With data science, organisations now have the power to use customer intelligence insights to take their businesses to the next level. Being aware of customer preferences is the first step in becoming truly customer-centric.
Build your data strategy
You should start by creating a clear and simple data architecture. This works as a base for how your business will access, manage, and utilise data in a scalable manner. Everything starts with a landscape of various data sources, followed by mapping this into a centralised, well-governed data lake that will help you to leverage your data in an effective way. A clear data architecture is essential from the very start in order to get cleaned, defined data. In time, the data will improve in quality and accuracy, and you will be able to avoid inaccuracies caused by any conflicting information.
Next to implementing a clear data architecture, there’s another must that you need to take into account. A data strategy needs to be developed together with other departments so that it aligns with the overarching business needs. It’s crucial that they understand the benefits, otherwise data science efforts will encounter roadblocks right from the start. Thus, make sure you get involvement, alignment, and approval from all stakeholders from the very beginning.
Turn your data science initiatives into a business game changer
How? Take data science seriously from the start. By doing this, you will prevent your data science initiatives from ending up as R&D projects. Reaching this stage makes it much harder to produce meaningful results. Data science models and artificial intelligence (AI) must be built into processes, applications and dashboards so that the benefits are easily accessible for end users. For this, you need to dedicate the right planning and resources. In turn, your employees will start to see the advantages of data science initiatives and then trust their potential. Next to that, it’s absolutely essential that the data science team has a good understanding of the business itself so that they can identify the potential benefits and translate them into different initiatives. It’s crucial to support your data science team so that they understand as much as possible about the industry and business. This way, they have a holistic view and can help the company grow.
Looking to the future: How much AI-driven will the payments industry be?
Everything will be AI-driven in ten years. In this sense, all business operations related to fraud and risk management, and optimisation processes will be powered and enhanced by AI. It will be a complete transformation of businesses as we know them today. The future looks good: there will be a lot of automation, allowing professionals to have more time to focus on complex scenarios that require human expertise in instances where machines can’t investigate further. We can also expect to see a change in dynamics: in time the AI automated services could reach a low-cost level, whereas the human-facing services could become the new premium standard. Automation is a natural step forward that will enable every part of a business to scale.
Are you attending MPE Berlin next week? I’ll be part of the session focused on Fraud and Security in 2019: AI & ML in fraud prevention, taking place on February 20, where I’ll talk about the next generation of fraud detection from 11:15. Be sure to join the session for more insights into the AI-driven payments landscape.
About João Moura
João Moura is the Head of Data Science at Payvision, with over 10 years of experience in top academic and business research. He oversees the company’s data science innovation projects, which involves the use of state-of-the-art data processing tools and techniques to successfully deliver highly complex data sets.
During his PhD studies in Artificial Intelligence, on Natural Language Processing and Machine Learning, João developed a solid theoretical and practical background. Currently, João is further expanding his expertise through a Big Data & Business Analytics MBA at the University of Amsterdam, and as a Program Leader within Payvision.
About Payvision
Founded in 2002 in Amsterdam, Payvision is at the forefront of the payments industry and it has been growing at a very fast pace. As global acquirer and merchant service provider, Payvision has built an independent, international acquiring network connecting payment service providers and their global merchants in the US, Europe, Asia and the Pacific. Payvision offers a global processing platform, 24/7 support, 150+ transaction currencies, a high-end reporting interface and a solid risk management solution. Through its data-driven payments platform, Acapture launched in 2015, Payvision completed its omnichannel package, supporting merchants to trade easier through a fast, secure processing platform for all transactions processed worldwide. This results in better authorization rates, less fraud, improved security and increased revenues for merchants.
Early 2018, Payvision has agreed to sell a majority stake of 75% to ING Group, creating a new synergy that will accelerate its ambitious growth plans within the payments space, extend its global network and broaden its payment product portfolio.
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