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Banking

BANKING’S UNACHIEVED DIGITAL POTENTIAL

BANKING

By Giuliano Altamura, Business Unit Manager Financial Services, Fincons Group

Pivotal role of digital integration hubs in transforming the banking sector

Today’s banking customers have radically changed the way they interact with service and good providers across sectors; they have come to expect a much more tailored and interactive customer experience that however is not always standard in every sector. Reports show that around 60% of banking customers use digital channels and that 80% of all customer touchpoints are digital[1], yet the financial services sector as a whole has been struggling to keep up with the pace set by digital-native industries like e-commerce.

While banks could once expect most activity, from querying balance to making transfers and payments, to take place at the branch or over telephone banking, nowadays bank customers expect to be able to access and query information over a growing range of touchpoints: Instant Messaging (IM), apps, the telephone, the web, emails, text messages and so on. They also expect customer experience across these channels to be consistently fast, interactive and organic, and, at the end, nice.

More touchpoints also mean more data to process, both in terms of query volumes and incoming data. The frequency with which consumers interrogate systems from apps, online portals, telephone banking or other channels is higher than ever before with older customers also becoming confident that digital transactions, bank transfers and payments are safe as well as cheaper or indeed free of charge.

These demands require new modern front-end systems that are attractive, engaging and fast but that also critically provide independence for legacy systems that remain in control of data and business operations. Pressure on line-of-business IT, and in particular on legacy back-end systems that are often business-critical, should not increase as these new front ends are developed or they risk underperforming on their core operations. In addition to this, front end systems need to be available 24/7, while legacy technology requires machine time for core activities and cannot risk losing capacity.

But the banking sector is not just facing the need to modernise its customer experience, it is also facing the significant competition of challenger and neo-banks as well as the mandatory need to comply with new regulation, specifically the Payment Services Directive 2 (PSD2) and Open Banking standards. PSD2, or ‘The Revised Payments Directive’ was designed to increase competition in the EU electronic payments market, provide consumers with more choice, and define rules and regulations for payment services that improve security and efficiency. The Directive also serves as a move towards Open Banking and obliges banks to share some of their customer data with other players. Fears are that this will lead to unpredictable peak request periods on banks’ legacy systems causing unnecessary delays and lack of efficiency, a view confirmed by a recent Polish survey where 91% of banks reported they felt the need to standardise APIs in connection with PSD2[2].

Making some customer data accessible to third parties should provide a clearer and more comprehensive view of customer spending habits but will also make protecting customer data much more difficult. Use of AI may come to the rescue on this front by helping to identify suspicious and unusual transactions or authentication patterns in real-time and flagging them up for prevention of fraud and money laundering.

In addition to these market pressures the banking sector needs to grapple with introducing blockchain, distributed ledger technology, Big Data, IoT, Cloud computing, AI, Biometric technology and Augmented/Virtual reality into its systems. Integrating all these new technologies is going to exacerbate the pressure on systems caused by data volume.

To lighten the burden on legacy systems and improve access to data and analytics Application Programming Interfaces (APIs) and microservices architecture are being developed. This should enable banks to leverage new technologies and additional external and internal data without overhauling their back-end systems entirely.  All these solutions, however, typically interface directly with back-end legacy systems where business critical information is stored. Not only is it important that historical data remains securely fenced off from any risk arising from providing third party access, but financial services businesses also need to protect their line-of-business systems  from unpredictable peaks in data access queries that could be caused by third party or partner activity, for example.

So how can banks protect their back-end legacy systems while also leveraging powerful API integration? A new, more efficient architecture centred around Digital Integration Hubs is rapidly standing out as a solution. In this new design, APIs read data extracted from a ‘data lake’ which is continually updated in near-real time by the legacy systems with an optimally flexible and efficient ingestion procedure, rather than by calling data up from legacy systems directly. The opportunity to feed in data from trusted third parties (TTPs), the IoT or Big Data therefore remains unfettered.

Digital Integration Hubs differ from Data Management Platforms because the latter were based on a batch approach to gather data from legacy systems. Typically, data in the Data Warehouse (DWH) was usually updated on a daily rather than in real-time basis requiring hundreds of extraction and ETL procedures to pull out legacy data and feed the data warehouse. As a result, traditional DWHs are useful for Analytics & Reporting but are inadequate for customer-facing Front Ends.

Digital Integration Hubs are designed to help reduce complexity of the API service layers and to decouple them from system of record data and line-of-business environments. They thus enable the consolidation of historical as well as new and real-time data into a single repository with 24/7 availability of near real-time data that APIs interact with instead of the back-end. In case of peaks in inquiries, load is thus managed by the data hub with no impact on back end operations.

The ideal solution should help realise all the above benefits and not just a portion. It should be speed and data agnostic as well as cloud-ready if not based and provide intuitive ‘Google-like’ search functions. Flexible end-to-end solutions are ideal to help insurers fall into step with the digitalisation of consumer and intermediary expectations without putting their systems at risk. Although slower off the mark than other more digital sectors like retail or travel, the banking sector is ready to embrace the benefits of customer experience improvement, compliance and operational efficiency. To do this, the sector need to ensure that its historical systems are protected both from cyber criminals as well as inefficiency. Data integration hubs stand out as the solution designed to provide a reliable connection between back-end system integration and new technologies that does not put systems at risk.

To find out more, please download the latest whitepaper from Fincons Group here: https://bit.ly/38iJx10

[1] Mckinsey & Company, The balancing act: Omnichannel excellence in retail banking, January 2019, https://www.mckinsey.com/industries/financial-services/our-insights/the-balancing-act-omnichannel-excellence-in-retail-banking#

[2] KPMG, PSD2 and Open Banking, March 2019

 

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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