James Buckley, VP and Europe Director, Infosys Finacle
The last few years have seen several non-banking companies such as technology majors, digital upstarts and FinTechs steadily expand their foothold in the financial services space. A 1recent study revealed that FinTechs now constitute about 33% of the financial services revenue globally. The recent Infosys Efma study found that non-banking players are perceived as drivers of innovation in the industry.
How will these developments evolve in 2019? In this article, we explore six business trends that will shape and influence banking over the next 12 months and beyond.
1. Open banking and business model innovation pick up steam
The 2018 EFMA Infosys Finacle Innovation in Retail Banking report offered a preview of the near future by predicting that the full-stack bank would give way to the distributor and marketplace models in the open economy. Business model innovation and open banking figure among the top trends in banking in 2019. Pushed on to this path by open banking legislation in 2018, banks will refine their vision and strategy this year, and also evolve their roles as product manufacturers, marketplace operators, distributors or a combination of the three.
Those choosing to build banking products will take the API route to co-innovate along with their ecosystem partners. In the role of marketplace operator, banks will expand their partnerships to bring the best products and services available on board. They will hope to emulate the likes of Amazon – which gets 40 percent of its business through recommendations – by using extensive analytics to improve their understanding of customer expectations and fulfill them with contextual and personalized offerings. The distributor model will get banks and non-banks to collaborate to create new value, especially through innovative use of channels. Banks will sell products through their own channels and those of third parties, including partners, Fintech companies, and even other banks.
2. Customer journeys move ahead
In 2019, banks will find a clear correlation between their quality of customer experience and business performance metrics. In July last year, the Institute of Customer Service published that banks with a higher UKCSI (United Kingdom Customer Satisfaction Index) score than the norm for the sector gained 8,675 current accounts on average compared to the rest, who lost an average 3,457 accounts. With customer journey mapping deemed a crucial customer experience skill, the customer journey will join the evergreen customer experience as a key trend for this year. In 2019, we expect customer journeys to transform for the digital age.
The customer journey will start earlier, at the point of intent or primary need, well before the customer starts looking for a banking product or service. So, from a bank’s point of view, the journey of a mortgage customer will begin when he goes house hunting, not when he approaches them for a loan. Teams in charge of the customer journey will become more diverse, drawing experts from domains ranging from user experience to consumer psychology who will be responsible for devising journeys for different personas. Banks will embed AI and harness analytics insights throughout to craft and design customer journeys that are highly customer-specific.
3. APIs begin to mature
In 2018, regulations such as PSD2 and the opening of banking, in general, allowed qualified third parties to transact on behalf of retail and corporate banking customers. As the primary lever of open banking, the API will mature further this year. With customers expecting greater value propositions from banks, the API will be key to customer-centric innovation and enhanced offerings. Findings of the EFMA Infosys Finacle 2018 research confirm this view. Respondents named open banking APIs as the top technology for the future of innovation, ahead of artificial intelligence, chatbots and machine learning.
Banks will also use APIs to integrate data from different external sources into various products and services on their menu.
The proliferation of APIs will create a challenge in the form of multiple API standards, creating a need for API brokers to help banks adjust to the situation.
4. Security faces new threats
Security will remain in focus in 2019.
Cyber threat will continue to intensify in 2019, as hackers target AI-based solutions with AI-based attacks in a reminder of the 2016 offensive on the Microsoft chatbot Tay that led to it sending out objectionable Tweets.
Hence we expect banks to up their investment in security tools significantly, with large institutions acquiring cybersecurity solutions to counter both deterministic and probabilistic hacking methods. 2A Deloitte survey predicts cyber monitoring and operations to account for the largest investments, followed by endpoint and network security. Apart from security technology, banks will need to invest in talent to combat the serious security skills shortage that will prevail in 2019.
This year, banks should be prepared to face unprecedented threats, such as malicious exploitation of their blockchain network’s hashing power. They must also strengthen governance to secure their and their customers’ interests in an increasingly open banking world.
5. Workforce changes culture
2019 will bring several changes in the banking workforce and culture as GenZ joins the ranks. Digitization will create demand for skills in cyber security, data science, and automation. Since universities rarely produce market-ready graduates, banks will need to bridge the gap with training and collaboration with academia to align curriculums with market needs, and expose students to real-world challenges through live projects. In 2019, we expect more action in the latter.
We also anticipate that banks will aspire to a well-rounded workforce that combines knowledge of business, industry, customer and organizational issues. Banks will need to adapt their culture to accommodate a multigenerational workforce of GenZ, millennial, and older representatives. Designing practices to enable employees to work on short-term projects is a key element here. So is embracing diversity, and banks will hire talent from different industries and disciplines. The new culture will value continuous learning and customer-centricity more than ever. Training programs will become more accessible on mobile devices. The trend will be to use real-time feedback to align banking practices with organizational objectives and increase customer-centricity.
6. Privacy is priority
With nearly 1.5 billion records getting compromised in the first quarter of 2018, protecting data and privacy will be a top priority in 2019. Conflicting mandates – to share customer data under open banking and to protect it under GDPR – will pose a serious challenge that will get compounded as transactions migrate from bank-owned channels to third-party modes.
Banks will have no choice but to take responsibility for securing data as it passes from their hands to third-party users, and also for meeting compliance requirements. This year, some banks will distinguish themselves in data and privacy protection by implementing the following: strong controls and governance, robust systems to capture customer consent, encryption and security standards, third-party authentication processes, real-time transaction processing, and security-by-design.
What Skills Does a Data Scientist Need?
In this modern and complicated time of economy, Big data is nothing without the professionals who turn cutting-edge technology into actionable insights. These professionals are called Data Scientists. Modern businesses are awash with data and many organizations are opening up their doors to big data and unlocking its power that increases the value of data scientists. Data is one of the most important features of any organization which helps to make decisions based on facts, stats, and trends.
As the scope of data is growing, data science came up as a multidisciplinary field. Data science is an integral part of understanding the working of many industries, complex or intricate. It helps organizations and brands to understand their customers in a much better, enhanced, and empowered way. Data science can be helpful in finding insights for sectors like travel, healthcare, and education among others. Its importance is increased as it solves complex problems through Big Data. With data science, companies are using data in a comprehensive manner to target an audience by creating better brand connections. Nowadays data science is taking an important and big prime role in the growth process of brands, as it is opening new fields in terms of research and experiments.
Let us know about the much-hyped role of a data scientist, the skills required to become one, and the need to take data science training.
Who is a Data Scientist?
Data Scientists are the individuals who gather and analyze large sets of structured and unstructured data. It combines the roles of computer science, mathematics, and statistics to create actionable plans for companies and other organizations. They gather, analyze, and process the data and then find the filtered results. Their work is to make sense of large, messy, and unstructured data using sources such as social media, smart devices, digital channels, emails, etc.
In other words, data scientists are analytical data experts who solve complex problems through technical skills to explore what problems need to be solved with available data. They are struggling with data all the time and experimenting via complex mathematics and statistical analysis. Usually, data scientists are required to use advanced analytics technologies such as machine learning, advanced computing, and predictive modeling. They use various types of reporting tools and analytical skills to detect problems, patterns, trends, and connections between data sets. Their goal is to provide reliable information about campaigns and consumers that help companies to attract and engage their customers and grow the sales.
A job of a data scientist is also known and advertised as a machine learning architect or data strategy architect. Data scientists generally require enough educational and experiential background of big data platforms, tools including Hadoop, Pig, Hive, Spark, and MapReduce and programming languages such as SQL, Python, Scala, and Pearl; and computing languages like R.
Skills Needed To Become a Data Scientist
To become a data scientist, it is recommended to have a master’s degree. This means a very strong educational background and the deep knowledge is must-required to become a data scientist. You must have a bachelor’s degree in any stream such as computer science, Physical science, social science, statistics, and mathematics or engineering.
The skills required to become a data scientist are categorized into technical and non-technical. Some of them are mentioned below:
● R Programming
R is specially designed for data science to deal with big data. It is generally preferred for data science to gain in-depth knowledge of analytical tools. Almost 43% of data scientists are using R to solve data problems and statistical issues.
● Python Coding
The most required technical skill to become a data scientist is having the knowledge of the most common coding language that is Python along with C, C++, Java, and Pearl.
● Hadoop Platform
It is the second most important skill to be a data scientist. This platform is heavily used in several cases. Hadoop is used to convey the data quickly to different servers.
● Apache Spark
It is becoming the most popular big data technology in the whole world. Just like Hadoop, it is a big data computation framework, but it is faster.
● SQL Database/Coding
With SQL database and coding, data scientists are able to write and execute complex queries in SQL.
● Data Visualization
A data scientist can visualize the data with data visualization with tools such as ggplot, d3.js and Matplottlib, and Tableau.
● Machine Learning and AI
Machine learning techniques include reinforcement learning, neural networks, adversarial learnings, etc. Along with it, supervised machine learning, decision trees, logistic regression can help you stay ahead from other data scientists.
There are also some non-technical skills such as Intellectual curiosity, Communication skills, Business acumen, Teamwork, etc. that can make you a successful data scientist.
Ready to Learn Data Science?
Data Science is nowadays a buzzing word in the IT sector. It has become an evolutionary technology that everyone is talking about. Several people want to become data scientists. It is a versatile career that is used in many sectors such as health-care, banking, e-commerce industries, consultancy services, etc. This career is one of the most highly paid careers. Data science careers have been always in high demand so the seekers have numerous opportunities to start or boost their careers.
It is a widely abundant field and has vast career opportunities because there are very few people who have the required certifications and skill-set to become a complete data scientist. You can gain these skills by enrolling in an online data science training program. By learning from industry experts, you will have a strong foundation of data science concepts. You’ll also be able to work on different data science tools and industry projects through a training course. So it’s the right time to get certification and grab the golden opportunities in the Data Science career.
This is a Sponsored Feature
How to use data to protect and power your business
By Dave Parker, Group Head of Data Governance, Arrow Global
Employees need to access data to do their jobs. But as data governance professionals, it’s our job to protect it. Therefore, we must perform a fine balancing act to weigh robust data protection against the productivity of workers who need the data to maintain business-as-usual working processes.
Data grows exponentially, and most organisations will admit that they simply don’t know what data they have, where it is, and the controls that exist around it. This creates 2 challenges:
- Burgeoning amounts of unstructured data makes the business increasingly vulnerable from external attackers or internal data breaches.
- Because data is the key to understanding a customer’s wants and needs, if the business can’t identify its data and unlock its value, it’s at a competitive disadvantage.
As a European investor and alternative asset manager, here at Arrow Global we take care of £50bn of assets and own a data estate exceeding 160TB. How we manage our data is key to our success. We understand the difficulties involved in opening up environments to allow people to work productively, while at the same time locking them down to protect our organisation.
When it comes to analytics, I believe that Arrow is highly proficient because we employ a talented team of data scientists. But even for us, the sheer volume of raw and processed data, that resides in both our structured systems and unstructured data repositories, has the potential to put our business at risk.
We know there’s always more that can be done to strengthen our security posture and ensure regulatory and contractual compliance, while at the same time using our data to drive the business forward.
Data protection isn’t just about compliance
For many organisations, data protection has centred on demonstrating compliance with the GDPR. At Arrow, our efforts have gone one step further to include our contractual exposure.
Being a more mature data organisation, we had previously tried to develop an application in-house to manage our data estate. However, with 160TB across the company in production data alone, we simply couldn’t achieve the scale we needed to handle the sheer volume of data. Of course, the volume is just the start – once you know what data you have, you then need to be able to categorise the data and put it into a structure, so the business can analyse it for a specific use case.
We knew we needed to go to market to find an industrial-strength data discovery product to replace our in-house application. By aligning our choice of product to our overall IT and change strategy, meant that ultimately, we ended up with a far better outcome than we’d anticipated.
Position data as both a risk and an asset
Data touches every part of an organisation, so when it came to building a business case for buying-in a data discovery software platform, we approached it in a way that would speak to different people at the same time. We did this by posing the question:
“What do we want to do with data in a way that is GDPR-compliant, contractually-compliant and enables us to better service our clients?”
These are the black and white tests of data governance – to recognise the importance of securing and protecting data. They’re applied in a way that enables us to commoditise data and use it to drive the business forward, by forcing us to consider how we would use the data – for example, creating value-based pricing for our clients.
In aligning the business case to initiatives that were already priorities within the boardroom, we knew that we’d gain the attention of the senior leadership team and it would be easier to get the buy-in and budget we needed. And in the end, everyone wins – we get what we need to protect the data, and the business gets to distil the data’s value to better meet our customers’ expectations.
Get visibility of data at scale
For us, things got really exciting once we were able to see all of our data at scale. We chose Exonar because it allowed us to discover our data in ways that other products couldn’t. And the interface between the user and Exonar meant that everyone – both technical and non-technical users – could understand the technology and the findings it revealed.
When we saw exactly what data was in the estate, where it was and who had access to it, data security became much easier and the risk of data being compromised was dramatically reduced. We can see exactly where the vulnerabilities are and restructure how our data is stored to strengthen security. Then over time, we can use search, workflow and analysis to optimise the infrastructure and continually identify new areas to improve.
Commercialise the data
From a wider-business perspective, once people can see the data, they can start asking “What if…” to query it and distil its value. But it’s more than just the data itself. It’s not uncommon for data relating to the same thing to exist in unconnected systems across the business. For example, customer interactions and incidents or events.
Exonar is capable of joining the dots in disparate data sets. By stitching these data sets together, we can get a better overall view of our customers and use the outcomes to think of new, different or better ways of serving them through enhancing or adapting our offerings.
Why other financial services businesses should also take a smarter approach to data
- By changing the way you approach data, you can use it to protect and power your business and the people you serve.
- By positioning data as both a risk and an asset, you elevate its position to give it priority in the boardroom. Ultimately, it’s data that helps the business make informed strategic decisions about how to strengthen its competitive advantage.
- By gaining visibility of data at scale, you can see exactly what data you have and where it is. This gives the business confidence about the actions needed to ensure it is secured in both a regulatory and contractually compliant way, and that people are doing the right thing with data at all times.
- And joining different data sets provides you with a single view of ‘X’ within your data, no matter where it is. Helping to support your wider-business strategy and priorities, it gives you the information you need to secure a business advantage and generate value.
How business leaders can find the right balance between human and bot when investing in AI
By Andrew White is the ANZ Country Manager of business transformation solutions provider, Signavio
The digital world moves quickly. From keeping up with consumer behaviour patterns, to regulation and compliance, the most successful organisations are always on the cutting-edge of technological developments.
However, when it comes to investing in artificial intelligence (AI), a hard and fast strategy does not guarantee a top spot amongst the league of tech greats. Instead, it pays to take a considered approach to balancing reliance on automated processes with a human touch. Why? Because creative and strategic thinkers are the true propellers of innovation; automation is simply the enabler.
The International Monetary Fund (IMF) developed the ‘Routine Task Intensity’ (RTI) index as a measure of which processes are likely to benefit most from automation. According to this metric, jobs requiring analytical, strategic, communicational and technical skills score low on the RTI index, while simple, repetitive tasks scored highly.
The lesson for business leaders here is simple; your digital investments are just as important as your stake in talent. When deciding which processes to automate, start simple, and remember to value the skills and potential of your people.
Keep customer-centricity at your core
Customer-centricity means that every business decision, dollar spent and new hire is centred on one question: how does this benefit my customer? Investments in AI are no different. To be truly successful, they must have a customer-focused outcome.
Where companies get this wrong is by implementing cost-saving measures or ‘copy and paste’ software that fails to improve the customer experience – often having the adverse effect.
Take the virtual chat-bot, for example; if implemented poorly, it can send your customers into a frustrating and seemingly infinite cycle of dead-ends. The modern consumer is far too digitally savvy for this shortcut, and will quickly move onto the next merchant offering a more seamless customer service experience.
To guarantee your investments are delighting rather than infuriating your customers, it helps to take an outside-in perspective of your business processes, aided by Customer Journey Mapping (CJM).
Before you commit to digital investments, CJM can trace and map each customer touchpoint, signalling pain points or conversion rates throughout their journey. These data-driven insights lead you to the areas that would benefit the most from automation, instead of implementing a broad band-aid solution.
Avoid the ‘set and forget’ method
When investing in enterprise-wide AI, the ‘set and forget’ method rarely works. Real transformation requires an ongoing dedication to refining and improving AI-driven processes, as well as adapting them to the evolving needs of your customers. This is the best way to achieve customer loyalty, by proving that your organisation listens to, and understands its users.
A human perspective is invaluable here, paired with process mining – a method that thrives on finding process inefficiencies – to create a consistent feedback loop of improvement.
During periods of uncertainty, customer loyalty is everything, so aim to protect it at all costs.
The power of your people
The rise of automation can be linked to the corporate world’s obsession with speed and efficiency. However, the psychology behind this goes deeper than being the biggest and fastest producer; it’s also about reallocating resources into attracting and retaining the brilliant minds that drive companies into the future.
When communicating digital change, it’s critical to highlight the valuable impact AI has on augmenting jobs; removing the burden of mundane, repetitive tasks and allowing for more strategic skill-sets to shine through. For lower-skilled workers, invest in upskilling or re-education where possible.
Successfully rolling-out digital transformation plans means that every employee across all tiers of your company understands the value of AI. The starting point here is education to achieve buy-in. Change communications must be accessible, constructive and value-focused, supported by key culture influencers who champion automation within teams.
Enterprise-wide buy-in is an important element of refining and improving digital processes, as cross-functional collaboration can offer valuable insights into common pain points or inefficiencies ripe for automation. Supported by process mining, collaboration provides a holistic view of how each investment will impact other processes. There is no point investing in automation that streamlines one process and makes another more people-centric, so be sure to take a balanced approach to your investments.
Remember, AI is not about creating an army of robot workers; it’s about increasing efficiency and productivity so that an organisation, and its people, can work smarter.
86% of UK businesses face barriers developing digital skills in procurement
A shortage of digitally savvy talent, and a lack of training for technical and soft skills, hinder digital procurement initiative...
ISO 20022 migration: full speed ahead despite recent delays, says new Deutsche Bank paper
Today, Deutsche Bank has released the third installment in its “Guide to ISO 20022 migration” series, which offers a comprehensive...
What Skills Does a Data Scientist Need?
In this modern and complicated time of economy, Big data is nothing without the professionals who turn cutting-edge technology into...
The importance of app-based commerce to hospitality in the new normal
By Jeremy Nicholds CEO, Judopay As society adapts to the rapidly changing “new normal” of working and socialising, many businesses...
The Psychology Behind a Strong Security Culture in the Financial Sector
By Javvad Malik, Security Awareness Advocate at KnowBe4 Banks and financial industries are quite literally where the money is, positioning...
How open banking can drive innovation and growth in a post-COVID world
By Billel Ridelle, CEO at Sweep Times are pretty tough for businesses right now. For SMEs in particular, a global financial...
How to use data to protect and power your business
By Dave Parker, Group Head of Data Governance, Arrow Global Employees need to access data to do their jobs. But...
How business leaders can find the right balance between human and bot when investing in AI
By Andrew White is the ANZ Country Manager of business transformation solutions provider, Signavio The digital world moves quickly. From...
Has lockdown marked the end of cash as we know it?
By James Booth, VP of Payment Partnerships EMEA, PPRO Since the start of the pandemic, businesses around the world have...
Lockdown 2.0 – Here’s how to be the best-looking person in the virtual room
By Jeff Carlson, author of The Photographer’s Guide to Luminar 4 and Take Control of Your Digital Photos suggests “the product you’re creating is...