AI, Metro Bank and Personetics

How AI and Big Data will Transform Banking in 2019

Metro Bank is already doing that with Insights, an in-app money management tool that gives customers complete control of their finances. It alerts customers when there’s not enough money to cover a likely spend, recommends a top up before an automated payment is due, flags if a customer has accidentally been charged twice and alerts the customer when there has been any kind of unusual activity.

Metro Bank personalises advice with Insights

Metro Bank has gone live with Insights, an in-app money management tool, which uses artificial intelligence (AI) to generate bespoke tips and alerts.

The feature was developed with Personetics, a developer of an opt-in tool for predictive analytics of users’ spending patterns.

As well as alerts that anticipate customers’ spending, users will be able to see a breakdown of where their money goes each month, delve into individual spending categories, and receive bespoke tips about how to manage their finances, based on their specific circumstances.

Predicting the ROI of AI – Pitfalls to AI Adoption in the Enterprise (Part 3 of 3) – Daniel Faggella

  • If you work for a results oriented company, the topic of ROI will be central to your pitch for funding/capital. In this post Daniel from does a really nice job offering suggestions to get your project started. Here are a couple snippets. A link to the full post at the bottom.
    • “If you’re a banking executive and all you do is read banking press releases before you spend money with AI vendors, you are inevitably going to be investing in the wrong places. It makes sense to get a deep understanding of where a return on investment is being garnered with AI in your sector.
    • They’re re-imagining their business in the era of Amazon, and recommendations is a very big priority for them. If that’s the case, it’s worth understanding which facets of recommendations within the world of eCommerce and brick-and-mortar retail are actually garnering a return.
    • So when we’re looking for an ROI as a business, we might want to look for a technology we’re already shopping for that might be served via an artificial intelligence vendor in a way that is going to be relatively easy in terms of integration.”

    AI and the bottom line: 14 examples of artificial intelligence in finance

    “Builtin put together a rundown of how AI is being used in finance and the companies leading the way. 

    Credit Decisions

    ZestFinance –

    Scienaptic Systems – –

    DataRobot –

    Managing Risk

    Kensho –

    Ayasdi –

    Quantitative Trading

    Kavout –

    Alpaca –

    Personalized Banking 

    Kasisto –

    Abe AI –

    Trim –

    Cybersecurity & Fraud Detection

    Shape Security –

    Darktrace –

    Vectra –

    Emirates NBD launches WhatsApp banking

    “Emirates NBD has launched the WhatsApp Business Solution to offer customised mobile banking services to its customers, claiming to be a first in the region.

    The implementation, carried out in partnership with Infobip, allows customers to interact with the bank through the chat for functions such as checking account balances, the last five transactions of account or credit cards, or the last credit card mini statement, temporarily blocking or unblocking cards, new cheque book requests and checking foreign exchange rates.” and LogicManager Partner to Offer Banks and Credit Unions a Modern and Seamless Compliance Management Solution

    • “The number of individual regulatory changes banks must track on a global scale has more than tripled since 2011, while banks and other financial institutions have spent nearly $321 billion on compliance enforcement actions (from 2007 until 2016).1 As regulatory compliance becomes increasingly burdensome and costly to manage, every financial organization is feeling immense pressure to update resource-intensive manual compliance programs. Organizations need technology-driven solutions that leverage automation and can be easily integrated into existing processes and software.
    •, a modern regulatory change management platform for financial services companies, has developed a strategic partnership with LogicManager, the leading enterprise risk management (ERM) software provider. With this partnership, LogicManager adds’s comprehensive, customized and financially-focused regulatory content to its integrations library within their platform. This partnership will empower compliance teams to proactively manage regulatory changes.”

    Global business value of AI in banking forecast to reach $300bn by 2030

    • “In 2018, the business value of AI was estimated to be $41.1bn, which includes the cost savings and efficiencies of introducing AI technology compared to keeping existing infrastructures and processes.
    • North America is tipped to be the biggest market for AI in banking between 2018 and 2023. The business value of AI in this region will go from $14.7bn in 2018 to nearly $79bn by 2030.
    • ‘Countries like China, Japan, South Korea, Hong Kong and Singapore are likely to drive the demand for AI within the banking sector over the next ten years,’ added Tait.
    • It’s not all good news: job losses and re-assignments will be common, IHS Markit estimates that by 2030, around 500,000 bank workers in the United Kingdom and 1.3m in the United States could be affected.”

    What’s the new weapon against money laundering gangsters?

    • “Money laundering accounts for up to 5% of global GDP – or $2tn (£1.5tn) – every year, says the United Nations Office on Drugs and Crime. So banks and law enforcement agencies are turning to artificial intelligence (AI) to help combat the growing problem. But will it work?
    • ‘Estimates suggest that not even 1% of criminal funds flowing through the international financial system is confiscated,’ says Colin Bell, group head of financial crime risk at HSBC.
    • In the UK alone, financial crime Suspicious Activity Reports increased by 10% in 2018, according to the National Crime Agency.
    • Once the system has learned legitimate behaviour patterns it can then more easily spot dodgy activity and learn from that.”

    To Battle Fin-Tech Upstarts, Big Banks Are Turning to—What Else?—Technology

    • “One recent report from the International Data Corp. found that the banking industry is the second biggest investor in AI technology worldwide behind retail.
    • ‘The financial industry is uniquely ready to make a move in the space and bring that computational experience,’ says Jason Mars, CEO of Clinc, a startup that makes chatbots for financial institutions like Barclays and USAA.
    • These virtual assistants are expected to trim hundreds of millions of dollars in service expenses as the technology becomes one of the dominant forms of addressing customer issues.
    • A study from Juniper Research in February forecast that industrywide cost savings from chatbots could reach $7.3 billion by 2023, a 3,400 percent increase from the estimated $209 million they are expected to provide this year.
    • ‘Banks have come to realize AI is not ready to work miracles yet,’ notes Forrester analyst Aurélie L’Hostis, who specializes in digital strategy for retail banks. ‘You really need to have a specific approach to drive your strategy. You can’t just launch a chatbot without knowing why you’re doing it.'”

    Jamie Dimon and Chase are all-in on AI

    If you’re involved in financial services you’re likely familiar with the name Jamie Dimon. If not, he’s the Chairman and CEO of JP Morgan Chase, and arguably one of the most influential leaders worldwide.

    A quick Google search will return references to “America’s Most Important Banker,” “Savior of Wall Street.” “Last CEO Standing.”

    With that context, I just stumbled upon Mr. Dimon’s shareholder letter dated April 4, 2019 and I wanted to share. He is clearly all-in on the benefits of artificial intelligence and machine learning.

    Here’s the message verbatim. I’ve also included a link to the full letter. The AI commentary is on page 34.

    “The power of artificial intelligence and machine learning is real.

    These technologies already are helping us reduce risk and fraud, upgrade customer service, improve underwriting and enhance marketing across the firm. And this is just the beginning. As our management teams get better at understanding the power of AI and machine learning, these tools are rapidly being deployed across virtually everything we do. We can also use artificial intelligence to try to achieve certain desired outcomes, such as making mortgages even more available to minorities. A few examples will suffice:

    • In the Corporate & Investment Bank, DeepX leverages machine learning to assist our equities algorithms globally to execute transactions across 1,300 stocks a day, and this total is rising as we roll out DeepX to new countries.

    • Across our company, we will be deploying virtual assistants (robots driven by artifi- cial intelligence) to handle tasks such as maintaining internal help desks, tracking down errors and routing inquiries.

    • In Consumer Marketing, we are better able to customize insights and offerings for individual customers, based on, for example, their ability to save or invest, their travel preferences or the availability of discounts on brands they like.

    • Technological solutions help us do better underwriting, expediting the mortgage or automobile loan approval process, letting the customer accept the loan in a couple of clicks and then start shopping for a home or car.

    • In our Consumer Operations, we are using AI and machine learning techniques for ATM cash management to optimize cash in devices, reduce the cost of reloads and schedule ATM maintenance.

    • And our initial results from machine learning fraud applications are expected to drive approximately $150 million of annual benefits and countless efficiencies. For example, machine learning is helping to deliver a better customer experience while also prioritizing safety at the point of sale, where fraud losses have been reduced significantly, with automated decisions on transactions made in milliseconds.

    We are now able to approve 1 million additional good customers (who would have been declined for potential fraud) and also decline approximately 1 million additional fraudsters (who would have been approved). Machine learning will also curtail check fraud losses by analyzing signatures, payee names and check features in real time.

    • Over time, AI will also dramatically improve Anti-Money Laundering/Bank Secrecy Act protocols and processes as well as other complex compliance requirements.”

    Wow!! That speaks volumes about where we’re headed with the potential for artificial intelligence and machine learning in banking and financial services.

    AI and video banking join forces

    I read a lot of these articles on a daily basis and it’s easy to identify the ones that are repeating the same old story to build SEO; and, I scrub those out so that you’re not stuck reading the same thing every day.

    This one is clearly different with the concepts of predictive routing and sentiment analysis…check it out.

    Predictive routing

    “AI-powered predictive routing engines can use historical performance data and match customer and employee attributes to predict which contact center agent is most likely to achieve targeted business goals.

    Next best action

    Contact center platforms such as Genesys leverage AI to suggest the “next best action” to agents in real time. This recommendation is typically based on an analysis of the customer profile, the type of inquiry they are making, and keywords being used in the conversation.

    Chatbot-to-human escalation

    In an attempt to keep their contact center agents focused on high-value transactions, many financial institutions have started to deploy chatbot applications for lower-value interactions. However, even as chatbot technologies are rapidly improving and can effectively address basic needs, they cannot establish a personal connection that can build confidence and drive customers to share more of their needs or invest more.

    Biometric identification

    Having a reliable way to identify a customer is a fundamental step in closing a financial transaction remotely, and is almost always a compliance requirement. In most cases, it includes verifications such as asking customers for their account number and other questions including address, birth date, and social security number.

    Real-time sentiment analysis

    Analysis of the video and audio streams will also dramatically enhance the assistance that can be provided to the agents. Facial expressions, body language, tone of voice, and keywords all reflect underlying states of mind, and uncovering them in real time feeds more informed suggestions to agents, who can then act more effectively.

    Simultaneous interpreting

    Speech recognition, automatic text translation, and speech synthesis are all making rapid progress. It’s easy to envision applications in the not-too-distant future that will combine these technologies with video interaction. This will enable participants to speak their own languages but see on screen or even hear a translation of what the other party is saying.


    Last but not least, contact center executives are constantly looking for ways to better collect and analyze the content of interactions to improve the quality and effectiveness of their services, provide more value to customers, and identify relevant post-contact actions. With speech recognition, the audio content of a video conversation can be transcribed into text, stored, and analyzed like any other text-based interaction channel.”