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Banking on conversational AI

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Banking on conversational AI 1

By Stephen Harcup, Head of AI Banking, EMEA, IPsoft

After years of incremental change, banks need a fundamental rethink of operations in order to thrive in a rapidly digitised and data-driven world.

Across the globe, the retail banking industry is fast embracing a mobile-centric customer experience. Investment in mobile technologies has led to a rapid adoption of digital banking to meet consumers’ constantly evolving demands. Meanwhile, consumers’ experiences in other industries are upping the ante for most banks. Recent research found that banking consumers have a stronger emotional connection to brands like Apple, Amazon, and Google than to their banks. These companies have the ability to blend experiences from the physical and digital worlds and that is what provides a good model for banks.

But in striving for the latest and greatest omnichannel solutions, banks may be tempted to embrace whatever new technology is available. This has led to a glut of banks implementing chatbots, which claim to bolster customer service functions with automation. In reality, these “bots” behave more like dated robots and are highly rigid in how they interact with customers, creating an experience that frustrates callers and prevents banks from truly serving their customers.

This need to bolster customer service offerings with technologies is especially pertinent today, in the aftermath of COVID-19 lockdown measures and the possibility of subsequent lockdowns. It’s now clear that many banks were overwhelmed by an influx of calls at the height of the quarantine, and they are now facing a potential resurgence as many cities and towns deal with local flare ups.

Yet, for whatever reasons call volumes spike, banks need to be able offer the same quality of service over the phone as they would in person. Banks can’t be expected to satisfy customers with rudimentary chatbots. They should instead look to the power of Digital Employees, which can help them meet scalable customer expectations in ways that are not possible with any other technology.

Adapt to customer needs

Conversational artificial intelligence (AI) forms the backbone of Digital Employees, a new technology that differs from chatbots in a number of ways – a primary one being their ability to handle customer going off script. Chatbots cannot determine what customers want if they change their mind mid-sentence, or introduce multiple issues or queries simultaneously. Chatbots tend to become confused and either provide the wrong answer or fail to provide any answer at all. This can be extremely frustrating for consumers, whose issues will only be worsened by the chatbot’s inability to resolve them.

Stephen Harcup

Stephen Harcup

Chatbots are built with a strict, formulaic interaction in mind. They cannot learn to answer questions beyond the way in which they were programmed, nor can they learn to resolve new issues over time. This inevitably leads to roadblocks that reduce net promoter scores (NPS) and decrease the number of interactions that can be resolved on first contact. Moreover, conversational AI agents will make it easier for customers to access their bank as they are “always on” and will never deviate from best practice or have a “bad day”.

Unlike a chatbot, digital employees can rapidly adapt to customer needs. Most importantly, in the form of customer intent. When a customer says, “On Wednesday, transfer £100 to Steve” the Digital Employee will understand the request. If the customer then adds, “Transfer it on Thursday instead” the conversational AI will understand what the user meant by “it”, react accordingly and transfer the money via the requested service without further clarification.

Digital employees can also prioritise the most important aspects of a request. Suppose that a customer says “I cashed in my loyalty points for a gift card and would like to know when it will ship. Also, there is a fraudulent charge on my account, so I’ll need to cancel my credit card,” chatbots would not know to respond. At best, a customer might find out when the gift card will ship. Digital employees are able to cut through the clutter and take immediate action on the fraudulent charge, while also recognising the second intent to know when the gift card will ship.

Help customers without restrictions

Learning is another key facet that differentiates Digital Employees from traditional chatbots. Chatbots, as rigid systems, don’t get smarter with time, nor can they provide assistance in the form of a whisper agent, which helps human employees answer customer questions and resolve problems faster and more efficiently.

The ability to handle the unexpected is key to driving NPS and first call resolution. The key differentiators for Digital Employees reside here. In fact, they can go beyond the automation of simple tasks, such as troubleshooting and password resets. Capable of helping customers unearth account details, process mortgage applications, and introduce new products, digital employees are quickly becoming a personal concierge for every customer.

They go further than an FAQ alternative, using Natural Language Processing (NLP) to better understand what the human is trying to say. Unlike a chatbot, digital employees will not come to a standstill simply because a customer asked more than one question at a time.

Built to provide solutions without stumbling

Chatbots and true AI – specifically conversational AI – should not be confused. They are not the same and will not provide the same results. While chatbots cannot handle the unexpected, Digital Employees can evolve with customer needs.

Powered by conversational AI, digital employees can decipher complex sequences, identify intent and provide solutions without stumbling or reaching dead ends. And, if there is a problem it cannot resolve on its own, the Digital Employee is smart enough to transfer over to a human customer agent, who can take the reins from there.

Ultimately, conversational AI technologies – in the form of Digital Employees – have matured and are ready to implement now. They offer the best opportunity in decades to establish new operational models. By pairing the existing strengths of traditional banking, with cutting-edge AI technology, banks can seize competitive advantage amongst a growing number of non-competitive forces. The speed and scalability of cognitive technology will release a constant stream of growth opportunities for banks that adopt conversational AI now.

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The Coming AI Revolution

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The Coming AI Revolution 2

By H.P Bunaes, CEO and founder of AI Powered Banking.

There is a revolution in AI coming and it’s going to render legacy data and model governance practices obsolete.

The revolution will manifest in three ways:

  • Automated machine learning platforms like DataRobot, H2O.ai, Dataiku, and rapidminer are making data scientists more productive. A lot more productive. One company told me that they were seeing 7x as many models from their data science group shortly after the implementation of a leading autoML platform. The increase in model output will quickly reveal bottlenecks in model validation, production implementation, and model operation and management.
  • The increasing popularity of tools aimed at “citizen data scientists”, local data literate subject matter experts in the business without formal data science training who nevertheless know a good model and a good use case when they see it, will turn a large percentage of technically savvy business people into model developers. Models developed by citizen data scientists will quickly dwarf the volume of models created by formal data science organizations adding further strain on existing procedures and revealing gaps in governance.
  • Availability of nearly unlimited capacity on demand for both data storage and computing power from cloud providers will lead to the proliferation of sophisticated predictive models that can learn from broad swaths of data; structured (your existing databases, for example), semi-structured (your documents), and even unstructured (such as images), sniffing out the data that is relevant to any one particular prediction or population. Demand for more, and different kinds of data for modeling, and the need to integrate model results into downstream dataflows and IT applications, will make data platforms and data flows significantly more complex, harder to manage, and increase points of failure.

What this all adds up to is an explosion in the volume of predictive models and of the data in motion in your organization. Where there were no models, there will suddenly be many. Where there was one model, you may find there are now hundreds. And the pipes providing data into and delivering results out of these models are going to proliferate. Operational and reputational risk from model failure will rise significantly as companies outgrow their existing data and model governance frameworks and legacy procedures.

Making this worse, many banks are starting from a weak position. The demand for more and better models (descriptive and predictive) has already led to a thicket of overlapping, partially inconsistent data flows to a multitude of models. Model outputs themselves have become part of the data flow to downstream data marts, BI, apps and even to other models as inputs. It is the rare organization that knows where all that data is coming from, where it is going, how it is being used, and can identify the potential impacts of changes to data and to the models that consume it.

Certainly there has been much improvement in recent years in data governance at most large organizations. Data quality, data standards, data integration, and data accessibility on robust platforms (increasingly cloud based) have all gotten better. And most organizations now have robust model risk management practices in place, to test and validate models before they go into production use.

But these worlds are about to collide. Data and analytics, once distinct and manageable separately are going to become inextricably intertwined. As brilliantly explained in a paper by several smart people at Google (“The Hidden Technical Debt in Machine Learning Systems”),​ we will rapidly reach the point where “changing anything changes everything.”

Take a simple example, what differentiates data on a client from a CRM system from data on a client created by a predictive model? The answer: nothing. Yet they are managed today by different groups. The former is typically managed by Data Governance, which is usually led by the Chief Data Officer. The latter is usually the province of Model Risk Management often found in the Corporate Risk Management organization.

But when model outputs become inputs to reports, to business processes, to critical operational or client facing systems, or to other models, they need to be governed just like any other data.

The perfect illustration of this challenge is in change management. Often you will find data change management in the chief data officer’s organization and model change management in the model risk organization. But changes in the data can, and often do, effect models in sometimes unpredictable fashion. And changes to models can change outputs and have major impacts to downstream consumers of those results if they are not prepared for the coming changes.

Managing them separately and distinctly will therefore no longer be sufficient. How to tackle this?

  • First and foremost, you must have a complete catalog of all models including metadata describing model inputs and their source and model outputs along with their destination and uses. There are a number of solutions now coming on the market for this purpose including Verta.ai, ModelOp, and Algorithmia.
  • Second, data management needs to expand to include not only source data but also all the results (predictions, descriptions) produced by models.
  • Third, model management too needs to expand its remit, not just focusing on model testing and validation prior to model implementation but also monitoring model performance and managing model changes after the fact​ ​.
  • Fourth there must be formal procedures for keeping model management and data management mutually informed and closely coordinated. Data cannot change without assessing model impact, and models cannot change without assessing data impact.

Organizationally, it may be infeasible to combine legacy organizations across traditional lines of responsibility. And it may be better to leverage existing expertise across model management, data engineering, data management, and IT. But a new partnership model, new tools, and new procedures will be needed.

The explosion in AI is upon us. To use AI safely and effectively you need to get your data and analytics house in order and make sure the right mechanisms are in place to keep it so. Regulators have taken note of the risks of poorly managed AI, and it is only a matter of time before they dictate minimum standards. Combining, or at least tightly coupling, data and model governance is where to start.

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How financial services organisations are using data to underpin future growth

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By John O’Keeffe, Director of Looker EMEA at Google Cloud

In addition to the turmoil caused by the COVID-19 pandemic, a significant decline in venture capital investment has left many financial services organisations feeling deflated, with others struggling to survive. According to figures from trade body Innovate Finance, investment in UK fintech organisations fell 30% in Q2 of this year, with smaller challenger firms and start-ups being the most profoundly hit by our current economic problems.

As a result, both challenger banks and more established players have had to pivot their strategies in order to maintain relevance and market share. Nonetheless, the outlook for fintech in the UK and further afield looks promising for the future. The reality of spending much of our time at home, and out of reach of brick and mortar services, means that many of us are becoming even more accustomed to digital banking for example. Recent analysis of finance application usage from Adjust, found that the average sessions in investment apps surged 88% globally, while payment and banking app sessions increased by 49% and 26%, respectively, during the COVID-19 pandemic.

However, the fact remains that investment in the sector is currently hard to come by. To help regain momentum, a review into the UK’s fintech industry was launched to identify opportunities to support growth across the industry. Data has – and will continue to – play a key role in this push for innovation, helping organisations spot gaps in the market, predict customer behaviours and ensure that the decisions they make are based on real insights. At such a critical time, enabling a data-led approach will help organisations ascertain exactly what is required to accelerate change and ensure the sustainability of the industry.

The financial services industry is a data-rich environment, giving organisations a potential goldmine of customer interactions, product performance and market trends. However, the difficulty often lies in bringing this into a coherent whole, and extracting the business insights required for long-term success. This is as much about strategy and accessibility as it is about technology. Fostering a true “data culture” where employees across the business, whether data experts or not, can access real-time intelligence that informs their day-to-day decision making in a positive way, is crucial. This may mean tweaking your onboarding and training programmes, identifying data evangelists that can catalyse others, or simply making data engaging and relatable for those who are new to the practice.

For many organisations, data is often stored within traditional business intelligence tools, third-party SQL clients or even just a simple spreadsheet, meaning that valuable data insights are siloed and often hindered by a bottleneck between a stretched analytics team and the rest of the business. There is also the all-important General Data Protection Regulation (GDPR) to consider, so data governance and having a clear view of where data is being housed, and for what purpose, is particularly pivotal.

With this in mind, it is crucial to have a “single source of truth” to bring various data streams together and enable real-time, self-serve insights to your whole employee base. As an example of this in practice, data is a great way to understand your existing clients more intimately and nip any problems in the bud early. By building a custom data dashboard incorporating, for example, number of support tickets issued, change in ticket sentiment and number of days to renewal, you can build up an accurate picture of account health and how this has changed over time. In combination with real-time metrics on which products and features are being used and how, sales teams can have more meaningful and accurate conversations with their customers, converting at-risk accounts into potential growth opportunities.

Given the dip in VC investment mentioned earlier, it is more important than ever for startups and scale-ups to do more with less and set a strategic roadmap that supports rapid growth. By using data to measure and action customer feedback, these organisations can be more agile in taking new products to market and making sure these are useful and address specific pain points.

Whether a fintech scale-up or an established name, it has never been more important to shift your operations to a more data-led strategy. With an uncertain outlook ahead for business across all sectors, making data the “single source of truth” can help to navigate market trends, identify new growth opportunities and simply make an organisation’s decision-making smarter and more efficient. Through data-driven innovation and growth, one of Britain’s most valuable industries can continue to thrive in the future.

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The Bank of England partners with Appvia to assist in the design, construction and assurance of a new cloud environment

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The Bank of England partners with Appvia to assist in the design, construction and assurance of a new cloud environment 4

The Bank of England has appointed self-service cloud-native delivery platform Appvia to support the creation of a new cloud environment.

The announcement follows a public procurement process which commenced in January 2020. The Bank of England will work with Appvia on design, construction and assurance of a modern, fit for purpose cloud environment.

During the two-year partnership, Appvia will be supporting development and project teams within the Bank in testing and deploying code in cloud environments, working with security teams to integrate the cloud into existing operational and security processes; and implementing information governance compliance so staff are able to collaborate safely and securely.

Oliver Tweedie, Head of Digital Platforms at the Bank of England, said, “We have selected Appvia as our Cloud Delivery Partner to help us realise the Bank’s cloud ambitions and unlock the potential of the Cloud. Appvia come with a great pedigree and a wealth of experience delivering Cloud services within government.  Working in collaboration with Bank Technology teams, Appvia will help us shape and build the future of Cloud services across our organisation – a key part of our Technology strategy.”

Jon Shanks, CEO and Co-Founder of Appvia, said, “This is an exciting opportunity to work with the Bank as it undergoes a step-change in its approach to the cloud. Harnessing innovative cloud solutions, such as containers and Kubernetes is a real business enabler for the Bank to streamline the software development lifecycle, ways of working and cloud operating model. We look forward to working with all stakeholders at the Bank of England to support its digital transformation journey.”

Appvia, which counts the Home Office among its major clients, is a self-service platform that enables organisations to scale their infrastructure quickly, securely and easily using services such as Kubernetes. In September, Appvia launched the world’s first developer-centric tool to enable teams to predict and control cloud costs.

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