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DO BANKS REALLY NEED TO CHOOSE BETWEEN OPERATIONS AND INNOVATION?

D

Caroline Hermon, Head of Adoption of Artificial Intelligence and Machine Learning at SAS UK & Ireland

It’s no secret that challenger banks and fintech companies are changing the banking industry as we know it.

The main issue for today’s banking leaders is not that customers are switching to new high-street providers, such as Metro Bank, or to internet and telephone banking specialists like First Direct.

The real threat is much more insidious – and much harder to combat. A wave of next-generation digital disruptors, such as Monzo Bank and Revolut, are launching completely new types of services that eat into the most profitable parts of traditional banks’ value chains.

Agility, flexibility and an insatiable appetite for innovation are powering the rise of these disruptors. Many traditional banks claim to value these same qualities, yet only a handful of today’s market leaders practice what they preach in their digital strategy documents.

Banks that can’t read the writing on the wall risk becoming obsolete and seeing their market share whittled away. With data-driven innovation speeding the industry towards a future of seamless, integrated, customer-focused services, it really is time to get real or get out of the race.

Overcoming barriers to change

Many traditional banks are so focused on keeping the lights on that they fail to execute their innovation goals. Keeping a bank running profitably while satisfying the regulators is no easy task. And transformation initiatives often get pushed down the priority list.

Moreover, in the race to deliver transformation, many banks fall at the first hurdle. Their main competitive advantage – the rich data they possess about every aspect of their customers’ financial needs – is typically siloed in multiple legacy systems and managed by different departments. The task of consolidating data and establishing a central hub for business analytics is expensive, time-consuming and diverts valuable resources from run-the-bank activities.

Resistance to change is another common barrier to innovation. Automation can be an unwelcome concept for decision makers, especially those who manage large teams and have spent years building up expertise around the bank’s legacy systems and manual processes.

But it doesn’t have to be this way. Banks that adopt a disruptor mindset recognize that machine learning and artificial intelligence are powerful tools. With the right approach, these banks can use the same technologies to both drive transformation initiatives and streamline day-to-day operations, creating a virtuous circle.

For example, analytics-powered automation is not just a tool to eliminate paperwork and reduce headcount. By freeing up time, it can also help employees focus on high-value transformational tasks, revealing new opportunities for product development and highlighting ways to build more customer-centric services.

Finding a winning formula

Traditional banks may find transformation painful. But when they do embrace change, the benefits can be huge. SAS works with many of the leading banks in the world today, helping them develop and execute effective analytics strategies that build on their success and strengthen their ability to compete.

Analytics is key to transformation because it’s one of the unique competitive advantages that large, well-established banks have over their new rivals. With millions of customers who trust and value their services, these banks can accumulate a vast amount of incredibly rich data about customer behaviour. 

Since modern analytics techniques such as deep learning are extremely data-hungry, the banks with the most data will be able to build predictive models that are far more sophisticated and accurate than their competitors. This could prove to be a decisive advantage as AI initiatives begin to take centre stage in transforming customer service.

Transformation in practice

Recent success stories illustrate the very real rewards that banks can reap from digital transformation. For example, SAS has helped RBS put timely, accurate and insightful data analysis at the heart of every decision and use customer feedback to improve its services. Now the bank is on track to become the UK’s No. 1 bank for customer service.

Similarly, Nationwide recently worked with SAS to enhance member communication. Using AI and natural language processing of customer emails, the building society identified the communication methods that produced a positive reaction – and those that created frustration. 

The analysis revealed that 26% of all interactions could be moved to an online process – reducing waiting times for customers while saving time and resources. What started as a proof of concept has now become a companywide initiative to use data analysis to streamline its back-office operations, develop new products and evolve its services. 

Examples like these show that with the right analytics strategy, banks don’t have to make a choice between keeping the lights on and driving transformation. When banks mobilize their talent, data and expertise, they can combine innovation with efficiency and keep the disruptors at bay.

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Banking

HOW BANKING IS USING AI TO PROCESS CUSTOMER FEEDBACK

By Dan Somers, CEO of Warwick Analytics

 

More banks are turning to practical AI to rapidly analyse customer conversations for sentiment and emotional intent to get the insight and automation they need to transform their customer service and operations.

Here we look at 5 ways in which banks are using AI to process their customer feedback more effectively:

 

Processing incoming queries more efficiently

AI can remove the need for manual review of each incoming query and enables banks to handle them effectively from the outset.

The analytics can facilitate a much smoother omni-channel experience for the customer by: identifying which channels your customers are best suited to – and which work best for specific types of interaction; understanding the causes of channel failure and what drives customers to switch; and reducing customer effort by delivering service in the customer’s preferred channel first-time.

As a recent example, at one bank we were able to reduce the maximum time to respond to a customer from 3 weeks to 5 days. The solution used AI and machine learning to automatically analyse and prioritise all customer emails in near real time and routed high-priority cases to a dedicated work queue for fast action.

 

Automatically identifying customer intent and emotion

When different people are voicing different issues, they will use different words and sentiments. Vital data is often missed with traditional models and manual processes. For example a customer at a bank might say ‘by the time they called back, the bank was closed’. The keyword would be flagged as ‘closed’, when in fact the main issue was the call back. There are also other limitations with using just keywords such as sarcasm, context, comparatives and local dialect/slang. The alternative is to analyse text data using ‘concepts’ instead of ‘keywords’. This can be done effectively with AI.

 

Fast tracking customer complaints and issues

With AI you can send complaints straight to the relevant team for a faster resolution. We’ve helped banks reduce resolution time by up to 3 days which really boosts customer retention.

Dealing with specific complaints manually involves using more and more case handlers. Routing complaints automatically and prioritising by issue and category is also difficult due to the nature of complaints i.e. unsolicited, long and sometimes multi-topical. As a result, manual classification is often impossible within an acceptable time frame for the unhappy customer.

Using the latest AI however, banks are now automatically classifying unstructured data to provide an early warning of issues that need resolving fastest. This can lead to better and quicker outcomes at a much lower cost.

 

Spotting vulnerable customers early

Under the Financial Conduct Authority (FCA) front-line staff need to be able to spot different types of vulnerability in customers and support them accordingly. However, the volume of communication is just too much to carry this out manually.

The latest in AI speech transcription and text analytics is able to automatically detect hints at vulnerability from conversations with customers. The conversations are automatically analysed by to detect emotionally-driven comments that indicate vulnerability such as a basic lack of understanding, likelihood of a disability and circumstances. These vulnerabilities are flagged to the relevant members of staff for action. Regulated firms can also accurately understand the drivers behind the vulnerabilities so products, services and communications can be reviewed accordingly.

 

Banks using AI during Co-vid 19

During Co-vid 19 many banks have customer service agents working from home and/or in strict shifts. There has been a move from voice to webchat for many to cope with these changes which brings its own challenges and opportunities. Post-C19, many of these situations are expected to stay in place or at least not revert 100% back.

AI is helping to serve customers better focusing on taking cost out whilst keeping CSat up and channel switching down by improving chat optimisation, email, complaint handling and chatbot supervision.

 

Case study: Improving customer loyalty

A major UK bank was looking to improve its customer loyalty. It was already using the latest

analytical tools including social listening, sentiment analysis and a large data science team

but they were experiencing limitations and not making enough progress. They were also interested to see what online feedback their main competitors were receiving.

 

A number of key recommendations for the bank were identified using AI analysis:

  • A 10% increase in CSat (c. £200m pa revenue) from operational improvement
  • Comparable best-in-class churn e.g. Nationwide is 25% lower
  • Online and mobile banking is a key issue, and is causing direct churn
  • Drivers of churn are mostly customer service, branch closures, marketing offers, interest rates and vulnerability issues
  • Early warning can help predict churn tactically and intercept likely churners
  • 28% of Tweets and potentially all non-voice queries can be automated. This could be a £20m pa saving
  • Business banking, current accounts and ancillary services have the highest churn, and insurance the highest negative advocacy
  • Mortgages, current accounts, savings and overdrafts cause the most attritional set-up
  • There are distinct patterns and opportunities to adjust customer services resources to reduce churn and costs

With AI, this level of insight can be set up in a matter of days, delivered in near real time and without the need for a data scientist to maintain the model.

 

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Banking

WHY BANKS NEED TO EMBRACE OPEN SOURCE COMMUNITIES

Nikolai Stankau, Director Business Development, EMEA Financial Services at Red Hat, the world’s largest enterprise open source solutions provider.

 

Banks and financial services have long been benefiting from using open source software, which is code that is developed in a decentralised and collaborative way. Open source software is cost-effective, flexible, is developed rapidly, and tends to have more longevity than its proprietary peers because it is developed by communities rather than a single author or company.  According to Red Hat’s own research, 93% of IT leaders in financial services state that enterprise open source is important to their organisation.

Alongside adopting open source products, which many banks already do, there’s opportunity for these organisations to have a greater influence in the development of industry software, by engaging in ‘upstream’ open source community projects.

 

The advantages of engaging in upstream communities

In open source projects, code is developed as a shared process by a community of thinkers and developers anywhere in the world. Collaborating directly with these communities – what’s known as ‘upstream’ participation – can give banks a major competitive advantage on their journey to innovate. From there, software can either be downloaded at no cost, or consumed via a trusted open source vendor that secures and stabilises the software to make it suitable for an enterprise to use. This is also known as the ‘downstream’.

A company that contributes its developers’ time and resources to an open source community gets rewarded with the output of hundreds of developers working on the same code. This leads to a magnification effect, by virtue of the fact you’re expanding your team many times over while also benefiting from a much more diverse pool of talent. The result is that organisations can be captains of the product development process and work together with the community to design features and functionalities that meet their needs and keep up with customer demands.

An added benefit for banks engaging in these communities is it provides a great access point for sourcing new talent, as well as helping to retain existing talent. Developers are attracted to organisations that engage in upstream development because it allows them to be at the forefront of open source innovation and new community-led initiatives.

It’s common for multiple organisations in the industry to come together and collaborate on a project, which can drive significant benefits for the community as a whole. A good example is Fintech Open Source Foundation (FINOS), which is a community set up by banks to promote industry collaboration, by delivering software that addresses common industry challenges and drives faster innovation. The concept had its origins in Symphony, a open sourced messaging and collaboration tool that was adapted and improved upon by developers from other banks, ultimately helping the company to become a major business valued at around $1.4bn.

 

Where to join forces versus compete

Although the benefits of engaging in upstream communities are manifold, some organisations have concerns around intellectual property as well as the productivity of developers contributing to open source projects rather than exclusively working on the bank’s own proprietary software. To this latter point – in reality, the development of new solutions and features built inhouse often requires many months, whereas product ideas shared in a community setting can be executed in much shorter time frames. As the saying goes, many hands make light work.

Regarding the essential consideration of IP and competitiveness: a lot of where banks can differentiate is at the application layer; in the services they develop and offer, rather than at the underlying operating system or middleware foundations – these tend to be common and standard, and are what empowers organizations to get to market as fast as possible. Thus the greatest opportunity for banks lies in platforms such as Linux-based Kubernetes, which is now the industry standard for container orchestration and one of the most important technologies used in the financial services industry. Kubernetes attracts many contributors from diverse organisations all over the world.

Some IT leaders also recognise structural roadblocks: transitioning an organisation to new ways of thinking and operating is a process that isn’t achieved overnight. Not all banks have the legal or tech mechanisms in place to be able to share their code externally, and company policies can prevent their employees from engaging in open source communities. In a heavily regulated industry, it takes time for some organisations to create the necessary changes before they can harness the potential of upstream communities.

 

The future is open

As the software ecosystem expands, and in the face of accelerated digital transformation driven by the ‘new normal’ of the COVID-19 pandemic, banks and financial services have the opportunity to evaluate how they can get involved in open source. There are many ways to do this: they can invest financially in communities, provide technical leadership and resources, or contribute code. With organisations under more pressure than ever to gain a competitive advantage, playing a role in open source communities will help them create better products, speed up time to market and position themselves at the forefront of financial innovation.

 

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