January 13, 2023

Financial Services Need for Digital Transformation Consulting

Financial Services Need for Digital Transformation Consulting

COVID dramatically accelerated digital transformation processes for businesses; 80% of customer interactions were digital during this period. However, within the same report in the financial services sector, executives reported almost double the demand of digitisation manufacturing than their consumer packaged goods industry counterparts did. Furthermore, as COVID has stabilised in the Western world the overall number of digital banking users has increased by 23% since the start of the pandemic; proving that European digital migration is becoming permanent.

As McKinsey approximates that 75-80% of transactional operations and up to 40% of strategic activities can now be automated, this creates a lot of growth and internal synergy opportunities.

Neobank challengers

Some organisations have completed their digital transformation by switching to non-traditional functions such as Monzo who use purely online contact points (which means no brick and mortar stores). This is clearly both popular, and successful, as over 6 million people currently bank with Monzo with 90% joining via word of mouth.

Nubank, a Brazilian bank, offers credit cards and personal loans to over 53.9 million customers. As the majority of these customers would not receive loans via traditional banks from a lack of credit history, Nubank uses its own algorithms and customer behaviour data to perform an assessment.

Social care and banks

As Moog and Slakoff argue “technologists in the financial sector have a significant opportunity to prevent abuse as well as recognize when it is happening and offer support”. Financial abuse is a powerful method that enables abusers to keep their victim in the relationship and diminish their ability to safely leave. Whilst banks have the ability to develop algorithms to detect suspicious behaviours symptomatic of financial abuse, Monzo provides a proactive response. This is through their in- app traceless messaging feature which allows customers to discreetly notify their bank of their situation or raise concern; a feature which has been used over 2,500 times since 2018. There is also the option to create a code word to alert the police.

Artificial intelligence

Within a customer-centric, bank call centres are now under more and more pressure to perfect their customer service and call handling. The digital transformation of processes, integrating AI and data analytics can potentially offer a successful solution to this pain-point. However, in a 2022 World Retail Banking Report, 70% of banking executives believe they lack foundational data analysis and AI capabilities to compete long term; contrasting with 75% of customers that are attracted to Fintechs’ cost-effective and seamless services.

AI and advanced analytics can help improve dispute resolution, maximise customer relationships, sustain margin pressures and shorter strategy cycles. In the future, during the dispute process, back end systems should be able to perform quick data evaluations:
● Assessing customer history
● Leveraging historical data patterns
● Customer segmentation via geographic information from mobile phone usage, online interactions and aggregated payment behavioural patterns
● Predictive modelling analytics to preempt issues and take a proactive approach when there is human error via the customers or the bank 
In addition to using human intuition, AI can be used to reach a positive outcome, quicker and more accurately than just people alone.

For example, eliminating target call handling times, banks can additionally enhance customer experience and in the long- term reduce the quantity of calls. For example, according to Dixon, 22% of repeat calls involve downstream issues related to the issue of the original call. In the same study, when Bell Canada focused on forward-resolution instead of reducing call handling times, they experienced a reduction of 16% for calls per event and its customer churn by 6%.

Voice AI

Alexa, Siri and Google Assistant have made consumers more accustomed to integrating and interacting with voice technology as part of their daily efficiency. In the same 2022 World Banking report, 50% of banking customers stated their most desired feature was a voice assistant in comparison to the 35% of bank executives who viewed it as a priority; creating a gap between customer needs and board expectation that competitors can potentially fulfil.

Mobile voice search
Voice AI

For example, in 2011 when Netflix ignored their customers by splitting its DVD and streaming businesses and increased their prices by 40%. As a result, their stock price fell by over 50%, they lost 800,000 subscribers and became one of America's most hated companies. Although Netflix is still undoubtedly a corner of mainstream culture, Disney Plus has recently surpassed Netflixs’ subscriber count. These gaps between boards and customers can be resolved through data analysis of the customer voice with AI and SERVQUAL assessments. As a result banks are increasingly utilising social listening tools to improve customer experience, competitive analysis, crisis management and product development.

In 2017 NatWest invested in conversational AI-based virtual assistants, Cora. In 2021, CEO Alison Rose said Cora was successfully handling 67% growth rate during 2020 and completing 40% of its nine million interactions without human intervention. Some of these features include:
● Changing details such as address, phone number
● Ordering a pin reminder
● Reporting a lost card
● Managing mortgages
This AI system is based on biological models of the human brain and neural networks to detect human emotion and react verbally in addition to physically. This form of AI combined with machine learning ensures their technology and service skills are continuously evolving with context awareness to encourage customer loyalty.

The cloud

One of the most significant ways in which technology can enhance traditional bank’s services is through cloud computing and services. From a 2022 survey on Public Cloud Adoption in Financial services, 62% of respondents indicated they predict their workloads to run in a cloud. A further 94% of banking executives (from an Accenture 2021 survey) project that 50% or more of their organisation’s business will be in the cloud within three years.

Typically because public cloud suppliers offer a variety of PAAS models to assist banks execute their possess operational models to improve revenue generation, contain costs, increase customer insights, and deliver market relevant products quickly and efficiently. This can also help to synchronise the organisation, remove data and operational silos between risk, regulatory, finance and more. When large data sets are migrated into one place, the organisation can process advanced analytics for more interesting and applicable insights. Deloitte explains more about top and bottom line benefits of the cloud in finance here.

As the cost to maintain mainframes has been increasing for the majority of banks (91%) over the past few years, many executives are starting to look for more cost effective means, especially as many legacy mainframes are between 5-20 years old; thus incurring more cumulative technical debt.

Royal Bank of Scotland- digital mortgage support

In collaboration with digital transformation consulting firm (IBM), the Royal Bank of Scotland created an AI-powered, cloud based platform called “Marge” which was directly built on the cloud. It was embedded in RBS’s existing data structures, with access to new data added every minute. Since Marge’s implementation, RBS has witnessed a 10% decrease in call duration and a 20% increase in customer NPS.

Better security from financial fraud

AI can provide beneficial preventative measures of fraud. In 2021, 10% of financial service providers said they used AI- based anti-fraud technology and within a year this tripled to 31%.

Unauthorised credit card transaction
Financial fraud

Machine learning and data-driven detection

Data mining of big datasets is an effective method of amassing and identifying patterns of fraudulent behaviour. Machine learning enhances the accuracy of this process.

Conversational AI for Transaction verification 

As conversational AI has the ability to construct dynamic and humanlike discussion, this inspires more consumer trust. This element also enables voicebots to collect data the systems require to verify transactions and consequently cancel fraudulent ones. A transition to voice-based verification provides a more accessible and convenient service for users as well. This can also be enhanced by push notifications for unusual account activity as it promotes a more immediate response from consumers.

Vishing scams

One potential preventative measure of vishing is creating a single distinctive brand voice with high quality TSS built on neural networks to reduce development time, maximise storage capacity and produce natural ‘human’ voice based on the large availability of sample data to combat fraudsters’ use of low quality TSS.

Biometrics for User Authentication

Vocal biometrics have over 100 unique identifiers within the human voice. Santander uses this feature by customers repeating the same phrase 5 times to detect vocal patterns, accents and if someone is playing a voice recording to access their account.

Talent needs

In 2022, banking executives cited the greatest demand for talent needs as cyber security skills and cloud solutions and many companies also underestimate their skills gaps, improving business technological capabilities and stacks enhances their talent retention and attraction.

As new technology is proven to enhance collaboration and communication in addition to shortening time consuming processes, talent is increasingly interested in organisations that utilise modern technology.

WeShape have an unmatched network of the top 5% associates in the digital transformation consulting industry to help fill the current skills and capability gaps in tech departments and specialise in helping banks to digitally transform to the necessary industry standard.

Our technical squads are pre-gelled teams ready-made to deliver specific products and projects, within advisory, leading and implementation roles. They can build a bespoke technical squad to support the delivery of your technical roadmap rapidly and at scale. We can work independently, on a fractional basis or as a blended team.

WeShape has one of the largest talent pools in the market and can deploy technical squads within two weeks. Our solutions are tailored, cost effective and designed for maximum impact with. One significant account development strategy is weekly or biweekly workstream reports to give a high level overview to both client and us to identify any blockers or issues and mitigate accordingly. Effective governance and process helping decreases project delays and extensions to ensure our customers are satisfied.

If you’re interested in booking a technical call to find out more about our technical squads to digitally transform, book a quick chat with WeShape.


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