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AI IS DRIVING A NEW FRONTIER IN BANKING – BUT THIS REVOLUTION IS EXPLAINABLE

By Hani Hargras, Chief Science Officer, Temenos

 

During the second half of the twentieth century, Artificial Intelligence (AI) was an idea that only featured in science fiction movies. Such films often depicted a future dystopia where humanity struggles at the whim of advancing technology. However, today we find ourselves living in a new frontier defined by data and automation. Central to this is AI, which is playing a major role across many sectors.

A report from Microsoft and EY analysing the outlook for AI in 2019 and beyond, stated that “65% of organisations in Europe expect AI to have a high or a very high impact on the core business.” In the banking and financial services industries, AI has already begun to vastly improve the customer experience. Important actions are being undertaken using AI on credit risk, wealth management and even financial crime risk assessments. Other applications include robo-advisory, intelligent pricing, product recommendation, investment services and debt-collection.

The latest Economist Intelligence Unit (EIU) report carried out on the behalf of Temenos, found that 77% of banking executives believe that winners and losers within the industry will be determined by how they embrace AI.

Rarely, if ever, will banks have faced the kind of immense strain than during this pandemic. They were already under pressure transitioning their operations to be compliant with social distancing guidelines and are now having to cope with record levels of demand from their customers. Online banking has seen a huge spike in activity following the closure of bank branches during lockdown and banks are also having to lend in unusual circumstances, requiring them to manage the twin pressures of increasing arrears and political pressure not to trigger defaults.

Hani Hagras

Banks must have the ability to adapt in order to deliver whatever is required to support the financial health of their customers. They need to quickly scale up operations and develop new digital products and processes in highly compressed timeframes. To cope with the huge demand for Bounce Back Loans in the UK, lenders need to provide simplified digital self-service user journeys.

To do this, they must urgently deploy technology that can support greater automation and efficiency and improve productivity. This technology not only exists but is already successfully being used to solve problems.

AI will enable banks to significantly accelerate digital onboarding, conduct eligibility checks and process loan applications. AI can centralise and enforce policy rules to ensure decisions are based on bank-specified criteria. This consistency in decision-making is hugely important in the current context as it reduces the need for manual intervention, which can slow down the processing of the huge volumes of loan applications. This technology can also play a key role in rapidly accelerating the onboarding of customers to digital banking services.

However, it must be recognised that the adoption of AI across business sectors has not come without its challenges. In a recent forecast, Forrester predicted a rising demand for transparent and easily understandable AI models, stating that “45% of AI decision makers say trusting the AI system is either challenging or very challenging.”

With the surge in AI usage will come much greater regulatory oversight. Transparency must be a key tenet of AI regulation and models that offer limited visibility and that don’t protect against bias should be regulated out.

Just as it’s important to be able to look under the hood of a car and understand how it works, so should banks be able to look at how decisions are made and understand how they are reached. Yet, transparency is not possible with traditional ‘Opaque box’ AI.

True ‘Transparent box’ explainable AI systems enable merging data based and human expert knowledge to generate models that are fair, safe, unbiased and highly accurate. XAI systems are highly transparent models, which could be easily analysed, understood and augmented by the business users. The XAI models can explain, in human language, how an AI decision has been made. Crucially, they do not solely rely on data, but can be elevated and augmented by human intelligence. This technology will be crucial in helping bank’s build trust with customers and regulators and identify issues as they arise.

The global pandemic is once in a century event and has demonstrated how indispensable agile and scalable technology is in responding to a crisis. With pressure mounting on the sector, the deployment of XAI technologies is key to enabling banks to help secure the financial wellbeing of its customers. This is a once-in-a-lifetime opportunity for banks to make the difference between a huge number of businesses staying afloat or going under

Technology

DATA DILEMMAS IMPACTING ESGS

Mario Mantrisi, Chief Strategy and Knowledge Officer, Kneip

 

It’s been well documented over the past few months that the COVID-19 pandemic has had a positive impact on ESG funds. ESG funds are typically portfolios where environmental, social and governance factors have been considered as part the investment process. Research from Morningstar shows that globally investors poured $45.6 billion into sustainable funds in the first quarter of 2020. In comparison, the overall fund industry saw outflows of $384.7 billion.

 

This trend is predicted to continue. Worldwide, attitudes are changing and a younger crop of ‘conscious investors’ with strong ethical views are now increasingly influential. Today, you’re less likely to see someone invest in an oil company. Instead, they are looking for innovative technology companies which fall under the ESG bracket, and more companies are entering this space. For example, United Nations Principles of Responsible Investment, which launched in 2006 with 100 signatories, now has more than 3,000 supporters, with a combined $100 trillion of assets under management.

 

With data now playing a fundamental role in the way funds, both ESG and typical, are managed, what role will data play in accelerating growth in this space? Although ESGs are doing well, we are seeing a critical issue which will determine their future growth – and it stems from data.

 

Across the board, ESG scores vary, and despite increased regulations from the UN, EU and individual countries’ regulatory bodies, there is no unified definition on what constitutes an ESG. This is why you’ll occasionally see oil companies pop up in an ESG fund. Tied into this, the way a lot of companies analyse data is biased toward larger companies who publish more data about themselves and are therefore likely to score higher in a fund manager’s ranking.

 

It’s clear changes need to be made to make it easier for fund managers to convert the interest investors are expressing in ESGs into proactive investments. The first change to be made is better sustainability reporting from companies. The second is improving data measurement and reporting. By making changes to these areas we will be able to accelerate the growth of investment in ESGs.

 

Let’s start with what we need from companies. Currently, most reporting on sustainability is aimed at stakeholders such as NGOs, which isn’t most relevant to investors. However, data management platforms can dissect and digest these reports to provide a reliable assessment of ESG performance. The state of play is rapidly improving, for example there are various EU directives and UK laws that require companies of a certain size to report non-financial information on an annual basis, but is this enough to attract conscious investors, driven by a sustainability motive?

 

Currently, many companies are missing out on potential investment from a host of conscious investors. To make themselves more desirable as a viable ESG option there are several steps that they can take to improve their reporting. Recent research from Harvard Business Review recommended the following:

  • Articulate your purpose: Companies should demonstrate their purpose within a society, not just their profits. When reporting they should clearly explain how they produce profits by providing a solution to problems people and the planet face. The easiest way to articulate this is by producing a Statement of Purpose
  • Improve engagement with stakeholders: Company reports should include an analysis that identifies the ESG issues that affect financial performance. Such a report is an effective way to demonstrate to shareholders and other stakeholders that the company recognises its role in society
  • Improve measurement in reporting: Investors want to know how ESGs affect society as a whole and are committed to investing in these impacts. However, most companies aren’t demonstrating the positive impact they’re having. For some, this will be because of a lack of framework available to report this. The UN’s Sustainable Development Goals (SDGs) provides a reliable list of objectives that companies should recognise when preparing stakeholder reports. The SDGs recognises 17 goals that the UN identified as necessary for a sustainable future, including eradicating poverty and hunger, ensuring responsible production and consumption, and promoting gender equality.

 

We are also seeing moves toward data standardisation when it comes to ESG reporting. Standards such as the Sustainability Accounting Standards Board (SASB) and the Task Force on Climate-Related Financial Disclosures (TFCD) leading the way. The European Commission has also blazed trail here, in pushing for the standardisation of ESG data, from the Non-Financial Reporting Directive, which entered into force in 2017, to the EU Action Plan on Sustainable Finance, which will impose ESG reporting obligations on European investors from 2021.

However, it will take time – possibly years – before we see more companies begin reporting around ESG in a structured and standardised way. Until then, fund managers wanting to satisfy the increasing appetite from investors for ESG, will need to find ways efficient ways to make sense of disparate pieces of information and spot ESG opportunities for their clients.

 

Tech and innovation needs to be at the forefront of how reporting platforms support fund managers so they can effectively advise their clients.

 

A combination of data-driven processes is needed to measure and analyse the complex and unstructured ESG data that is available today. Technologies such as artificial intelligence and expertise in handling Big Data make it easier to analyse ESG data. In addition, machine learning and natural language processing (NLP) allows for algorithms to infer context as they sift through a variety of sources, such as annual reports, NGO reports, academic papers, regulatory and legal disclosures to assess a company’s sustainable credentials and performance. Furthermore, using these technologies removes biases and allows fund managers to review a much broader range of sustainable funds available to them.

 

It’s clear that ESG growth is going to continue to rocket as investors across the world become more conscious of their impact and look for ways to invest money more sustainably way. Smart fund managers will augment their own skills with the right data management platforms so that they and their clients can ride this wave of growth.

 

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Technology

SIX PILLARS FOR A SUCCESSFUL CLOUD

by Giuseppe Paternò, IT Infrastructure Architect, Security Expert, and Cloud Solution Guru

 

COVID-19 pandemic is pushing many companies to rethink their fundamental assumptions about where their people and IT assets need to be deployed.

With a substantial increase in the number of businesses now operating from their employees’ homes, and organisations leaders realising that their office buildings are now somewhat unnecessary until a vaccination against coronavirus is widely distributed, the cloud has played an increasingly important role in IT operations.

For many, cloud migration has had to happen quickly, and at a large scale in order to cope with the increased pressure on IT infrastructure during the Covid-19 pandemic. At such a turbulent and challenging time, it’s never been more important for businesses to ensure they have the correct IT infrastructure in place.

Cloud projects can range from building large private clouds to moving everything to the cloud. As I found when drawing upon my 25 years of experience in IT, however, moving applications to the cloud is the starting point for the cloud journey.

Therefore, I’ve identified six pillars for delivering successful cloud projects – by keeping these rules top of mind, companies can focus the project on the right solution, and deliver results for the business.

 

  1. Use open standards and open source

Giuseppe Paternò

Embrace open source and open standards as much as you can in your applications. Most open source products (excluding some databases or caches) can be automated and this makes them portable. If you choose a database-as-a-service, make sure it uses common protocols such as MySQL or Postgres, so that you can export data and import elsewhere.

You will have fewer update nightmares, more security, less vendor lock-in, and better portability across systems.

 

  1. Move everything non-strategic to the cloud if it reduces total cost of ownership

For example, a good first step is to move collaboration (mail, documents, VoIP, drive sync) to cloud services and ensure high security standards. Embracing cloud collaboration also gives you the option of cloud identity management, and this will ease cloud migrations.

Ensure SaaS providers will let you export the data in an open format. Check prices and the fine print before you buy – and only embrace SaaS products if they cut costs.

Applications with tight hardware integrations or with highly confidential data should remain on-premise.

 

  1. Keep it simple when you can

Over-engineering will create solutions that are difficult to manage. I cannot stress this enough. I know that many applications are complex, but don’t fall into the temptation of making things more complicated. It’s an easy mistake to make.

Only make things complicated if the extra effort will deliver tangible economic or competitive advantages.

 

  1. Automate whatever you can

Open standards for data and open source will help you avoid any lock-in and give you freedom of choice. Using containers to distribute and run the applications, along with an automation system, will create a process for quickly moving your application makes it easier to move applications across clouds or back on-premise.

If you can’t re-engineer your application, just lift and shift virtual machines (VMs) with legacy workloads to an infrastructure as a software (IaaS) provider.

Understanding how much uptime your applications need will help you save money – don’t pay for service levels you don’t need. You may be surprised to find that basic SLAs will work for most internal applications.

 

  1. Take a zero-trust approach to security

Trust nothing that’s crossed public or semi- public networks. Nowadays there is no clear divide between an internal office network and the Internet. In the most extreme cases, an office network is just a transit network, just a bit more secure than a home network.

Traditional firewalls are no longer effective, and you should apply protection as close as possible to the application.

But the work-anywhere model has some upsides. The office will just be a meeting place and users will be able to work from anywhere. Looking a bit further ahead, devices like the iPad Pro, with a keyboard and embedded trackpad, can replace PCs for standard users. These devices are easier to manage with a mobile device management (MdM) and less vulnerable to viruses and malware. The keyboard and the trackpad can be fantastic when using a remote terminal with two factor authentication to connect to legacy applications.

PCs and workstations will only be for advanced users who need proper computing power – for example, developers, or those who run local scripts or financial excel formulas with external data.

 

  1. Keep multiple backups in open formats

Last but not least, have one or many backups or replicas of all cloud data in an open format. I’m really paranoid about this. If you have a copy of everything, and you automated everything, you will be able to rebuild your entire infrastructure easily. Disaster recovery will be a piece of cake: you will be able to restore either to another cloud or rebuild on-premise.

No two cloud projects are exactly alike, and cloud solutions can be complex, with each project needing to meet specific business needs and work within its own unique parameters. The challenge is to discover the right path to success, which may require implementing organisational changes.

However, by implementing the six pillars for delivering successful cloud projects, the payoffs for companies that follow the right path are huge – which include increased business agility, lower or rationalised costs and better focus on the core business.

 

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