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SUSTAINABLE AI: WHY IT’S GOOD FOR BUSINESS

By Nick Dale, Senior Director, Verne Global

 

Society at large is becoming increasingly aware of its environmental impact, recently highlighted by the crystal-clear water in Venice’s canals and the Himalayas being visible across smog-free skies from over 120 miles away, amidst the global shutdown. This concern has also extended to the finance industry, with environmental, social, and governance (ESG) criteria rising in importance for both business and investment decisions.

At the same time, the financial sector has also been a major adopter of another significant trend – the use of AI and machine learning to improve efficiency and results. AI is particularly useful to the finance industry, from optimising asset portfolios and underwriting loans, to assessing risk and spotting fraud. In fact, the financial sector is leading the UK in the use of AI, with the great majority of banks, insurance firms and other financial institutions using such technologies.

Surprisingly, these two trends may be at cross purposes because of AI’s hefty carbon footprint. Training one deep learning model for natural language processing can emit more than 626,000 pounds of carbon dioxide equivalent, which is nearly 5 times greater than the amount generated during the entire lifetime of a car (including the manufacture of the car itself), per 2019 University of Massachusetts research.

But sustainable AI innovation is possible if financial services organisations begin to understand why AI can have a negative impact on the environment, and what they can do to minimise that impact.

 

Nick Dale

AI’s appetite for power

The field of AI has been growing in leaps and bounds in the last decade, and no where more so than the finance industry. Financial institutions like Goldman Sachs, Morgan Stanley, and S&P Global are routinely using AI tools like Kensho’s for investment insight. Kensho’s algorithms can process 65 million question combinations, analysing over 90,000 world events – such as political events, economic reports, and monetary policy changes – and their impact on asset prices. Forbes reports that traders with access to Kensho’s AI-powered data were able to foresee a protracted drop in the British pound in the days after Brexit.

But as AI technology grows and develops, the computations behind it are also increasing in size and complexity. There has been a 300,000-fold increase in the computations required for deep learning research from 2012 to 2018, according to analysis conducted by the Allen Institute for AI. On top of that, these AI computing platforms can sometimes run 24-hours a day, necessitating days and even weeks of processing, plus trillions of attempts, to get the numbers lined up. As a result, these applications consume an enormous amount of energy in order to function, and require significant and constant access to power.

The carbon cost of AI becomes even greater when you factor in the energy required to keep computing equipment cool in order to prevent overheating that can impact performance and damage equipment. In a conventional data center, at least 40% of all energy consumed goes towards cooling.

 

Steps to minimise carbon impact of AI

Green – or greenwashing?

For businesses looking to reduce the environmental impact of their AI, the first step is to check the green credentials of the cloud providers and data centers that power these applications. Despite the “green” label, there’s no guarantee that a cloud provider or data center is powered entirely, or even partially, by green energy. Instead, these green claims can be more akin to a “carbon offset” programme, with energy providers offsetting the carbon they produce through tree planting or other similar programs.

Renewable energy sources

Instead of greenwashing, make sure that the data centers housing your AI compute are actually powered by renewable energy. In many Nordic countries, data centers are powered by renewable energy sources like hydroelectric and geothermal power. Iceland, in particular, uses 100% renewable energy with no nuclear power. These renewable energies are much less harmful to the environment because, unlike fossil fuels, they don’t cause pollution and don’t generate greenhouse gases. Not to mention, renewable energy is based on natural resources that can be replenished within an average human lifetime, as compared to fossil fuels, which can take thousands—or even millions—of years to replace.

Data center location

The next step is to look at the location of your data centers. Over 80% of compute doesn’t need to be near the end-user, and in those situations, choosing data center locations in cool climates has a significant impact on carbon emissions. In such cases, AI compute can be located in places like Iceland, which can utilise free-of-cost, natural cooling, due to its year-round cool, temperate climate.

This is in stark contrast to data centers located in hot climates, like Arizona in the US. With average high temperatures of 40° Celsius in the summer, data centers in climes like these need high-powered cooling systems in operation around the clock, often supported by up to 4 million gallons of water a day used to absorb heat through evaporation into cooling towers. As a result, when it doesn’t affect performance or accessibility, housing AI compute in data centers with natural cooling seems like an environmental no-brainer.

 

Better for the environment – and for business

As much as the financial sector is starting to embrace sustainability as a key ESG criteria in their corporate strategies, some may still view such efforts as an added cost to the expense side of the balance sheet. But the truth is, green AI presents financial services firms with an opportunity to align profit with purpose. By housing the servers that train AI models in data centers powered by renewable energy sources – connected to a reliable power grid –, businesses can substantially reduce energy expenses and benefit from predictable pricing.

As well, choosing locations with year-round, cool climates that allow natural cooling of powerful AI servers further minimises energy usage. When it comes to green AI, reducing environmental impact also lowers energy demands and costs – something that’s well worth the investment.

 

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

BANKING’S SECOND WAVE OF TRANSFORMATION: INTEGRATING THE CLOUD-ENABLED FUTURE BANK

Keith Pearson, Head of Financial Services EMEA, ServiceNow

 

The last six months have seen significant changes to the financial services landscape, with operational resilience, economic recovery, cost reduction and an acceleration of digital transformation key themes emerging from the industry.

At the start of this crisis, much of the banking industry was in a different position to many businesses. The 2008 recession spurred a need for improvements and combined with the emergence of tech-savvy fintechs, the industry has seen a major shift as customer expectations have adapted. The pandemic has forced organisations to accelerate innovation already part-underway in the banking industry.

As banking experienced its first wave of transformation, institutions focussed on customer engagement, uniting physical and digital channels for an improved customer experience. Banks invested heavily in front office digital technology, creating visually appealing mobile apps, engaging online banking experiences and technologies for bankers to personalise customer engagement.

However, this digital engagement layer is not enough. Regulations like PSD2 reinforce the necessity to remain compliant, adding additional pressure to the digital transformation process which in turn has been accelerated by COVID-19. Banking is therefore in the midst of its second wave of transformation, where financial institutions are creating and seeking out critical infrastructure to better connect underlying middle and back office operations with the front office, and ultimately, with customers.

 

Keith Pearson

A disconnected operation

Many financial organisations are still struggling because they have yet to streamline, automate and connect the underlying processes that are enabling customer experiences. Which poses the question: why is connecting operations so difficult?

In most cases, multiple systems are still glued together by email and spreadsheets to track end-to-end status. Around 80% of a middle office employee’s time is spent gathering data from systems to make a decision, with only 20% spent actually analysing and making the decision.

The disconnect negatively impacts customers. For many, experiences like opening a bank account or getting a mortgage involve clunky, manual processes riddled with paperwork and delays. When front and back office employees lack the ability to seamlessly work together, customers can be asked for the same data multiple times, elevating frustration.

Customers have little patience and can be inclined to publicly broadcast problems when left unresolved. In a world of social media and online reviews, this could be detrimental to a company’s reputation.

With digitally native, non-traditional financial services players gaining market traction by offering a seamless customer experience, maintaining satisfaction is crucial for traditional banks to ensure that customers don’t switch. Banks must focus on making it easy for customers to do business with them by offering faster cycle times with more streamlined operations.

 

The fintech effect

Fintechs and challenger banks like Starling have shown what connected operations can do, having been built with digitised processes from day one. Modern consumers expect round-the-clock service from their bank. As financial institutions look to the future, developing a model of operational resilience that is capable of withstanding unforeseen issues, like power outages or cyberattacks, is critical to minimising service disruption. Having connected internal communications between front and back office staff means customers can be notified about any problems, how they can be fixed and when they might be resolved, as well as receiving continuous progress updates instantaneously.

Automation can go a step beyond this. Today, customers expect companies to not only do more and do it faster but to prevent problems arising in the first place. With connected operations and Customer Service Management (CSM), banks can proactively fix things before they happen and resolve issues fast, enabling frictionless customer service and replicating the ‘fintech effect’.

 

What about compliance?

In the European Union and the UK, PSD2 and the Open Banking initiative are giving more control to the customer over personal account data. Digital banks such as Fidor and lenders like Klarna are seeking to reinvent banking by offering customer-centric services. But the process of streamlining underlying operations is not simply about providing customers with the fintech-esque experience. More than 50% of a financial institution’s business processes are also impacted by regulation.

Financial services leaders are focussing on streamlining and taking cost out of business operations while also placing importance on resilience. Regulators are pushing banks to have a firmwide view of the risk to delivering their critical business services.

Banks must invest in digitising processes to intuitively embed risk and compliance policies, which are generally managed separately and often manually from the business process, leading to excessive compliance costs and risk of non-compliance. With the right workflow tools for monitoring and business continuity management, banks can minimise disruption by gaining access to real-time, actionable information about non-compliance and high risk areas, encompassing cybersecurity, data privacy and audit management.

Increasing openness of financial institutions to regtech solutions, or managing regulatory processes in the industry through technology, will prove key during this second wave of transformation. Banks will increasingly move away from people and spreadsheets and toward regulatory solutions that provide a real-time view of compliance and provide an end-to-end audit trail for Heads of Compliance, Chief Risk Officers and regulators.

With a unified data environment aided by technology, financial institutions can drive a culture of risk management and compliance to improve business decisions.

 

Riding the wave

The banking industry is still in the midst of its second transformation, and the pandemic hasn’t made it any easier. But riding this wave and successfully digitising processes to connect back and front office employees will present a profound difference to customer service.

The bank of the future will be frictionless, digital, cloud-enabled, and efficient; interwoven into the fabric of people’s lives. It will continue to be compliant and controlled but will deliver those outcomes differently, with risk management digitally embedded within its operations.

Demonstrating the operational resilience of its key services will not only drive customer confidence but will also provide a greater indicator of control to regulators and the market, adjusting overall risk ratings and freeing up capital reserves to drive more revenue and increase profitability.

The institutions that will thrive in this increasingly digital and connected world are the ones that are actively transforming themselves and the way they do business now, by taking learnings from fintechs, following regulations and paving the way in defining the future of financial services.

 

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