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Although quantum computing is still in its infancy, financial institutions can benefit from considering future use cases, setting up experiments, and skills-building. For Mastercard, that means seeing how quantum could help personalize rewards programs. 

Mastercard has partnered with D-Wave to research how quantum computing could supercharge its loyalty and rewards programs.  

Quantum – an emerging computing paradigm that promises to perform calculations at blistering speeds – is well suited to the challenge of sorting through the gobs of customer data that financial firms use to offer incentives to their customers. That’s why Mastercard has begun experimenting with how it could use quantum computing to help issuing banks and merchants better customize their loyalty programs. 

“Some of these retailers have customer bases in the hundreds of thousands, with hundreds of rewards programs,” Mastercard’s vice president of AI and machine learning, Steve Flinter, told American Banker. “So, how do you give which reward to which customer, and at what time? It’s really a hard problem to solve, even with the best technology that we have today.”   

Using data on payments, sales, redemption, demographics, and hundreds of other sources related to how consumers engage with incentives, Mastercard could help its clients shape their loyalty programs.  

Beyond its work with rewards, Mastercard also recently announced that it plans to issue “quantum-resistant” contactless credit cards that can maintain their encryption even when up against quantum computers.  

“Quantum computing is an emerging technology that can be revolutionary for our industry,” Flinter said. 

To that end, HSBC, JPMorgan, and Ally are also experimenting with ways to use quantum computing technology for their benefit, like improving the speed and precision of risk analysis. Meanwhile, Banque de France is preparing to protect itself against encryption-breaking quantum tactics and Credit Agricole just successfully completed two real-world experiments that found it could achieve “faster valuations and more accurate risk assessments” using quantum techniques. 

While money mule accounts don’t generate direct losses for banks, they can still have negative impacts, including maintenance costs and regulatory scrutiny. Banks should take advantage of advanced analytics and diverse data (including dark web intelligence) to help protect themselves. 

Money mules – people who receive and move money stolen from fraud victims – are particularly pernicious for banks. They’re often a key player in authorized payments fraud, money laundering, and other scams, but their accounts are difficult to identify (often because the mules themselves are unwitting pawns for criminals).   

A new survey by NICE Actimize found that financial institutions’ fraud leaders consider money mules one of their top five challenges. Similarly, more than 80% of fraud execs believe that more can and should be done to mitigate mule activity at their institutions.  

Banks have incentive to root out these accounts: They’re costly to maintain, aren’t profitable, and can expose FIs to regulatory scrutiny for enabling money laundering. While a mule account may not raise any red flags when it’s first opened, banks should rely on advanced analytics, behavioral biometrics, and diverse datasets (including dark web intelligence) to continuously monitor accounts, and flag potential risks.  

“Monitoring doesn’t stop at day zero,” the report recommends. “Data collected at the beginning serves as a solid foundation for continuous due diligence and monitoring. Think of it as a never-ending background check.” 

Flagging fraud and money laundering – including potential money mules – is one of the most promising (and well-funded) applications of AI in financial services; you can find more of Insights Distilled’s coverage of this critical topic here. 

As the world undergoes the largest intergenerational transfer of wealth in human history, financial providers need to offer the digital access and real-time transparency that younger clients demand. 

Wealth management is at a time of major transition, and providers need to ensure that their technology infrastructure and features keep up.  

That’s why Masttro, a wealth tech company focused on serving ultra-high-net worth families, just raised a $43 million round of funding led by FTV Capital, with participation from Citi Ventures.  

Masttro’s software uses advanced data processing and analysis to provide digital access and real-time visibility into client portfolios and total net worth. Its platform enables financial advisors and family offices to better and more efficiently serve their customers.  

“We like to say, ‘Why settle for 80% of your wealth being visualized?’ Managers – and end clients – should strive for a 100% view of wealth,” head of marketing Michael Melia told Insights Distilled. “Masttro is the all-in-one aggregator, synthesizer, and visualizer for every kind of asset class in every region around the world.” 

For example, Santander’s high-end wealth management division uses Masttro’s software to automatically aggregate its clients’ financial and non-financial assets, eliminating human error, saving the bank “hundreds of man hours,” and ultimately allowing its bankers to deliver better advice, faster.  

The software also helps advisors serve younger clients according to their preference: Digitally and in real-time. With an estimated $84 trillion in wealth expected to transfer to younger generations through 2045, it’s critical for wealth management providers to equip themselves with technology that allows them to adapt to changing expectations. 

Stakes are incredibly high for financial services firms undergoing digital transformations, and yet they often flounder. To boost results, tech execs need to create a culture of empowerment through incentives and clear messaging. 

Nearly all banks are using technology to rethink their processes, features, and operations, but the road to reinvention is bumpy: A stunning 70% of leaders report witnessing a banking transformation that has underperformed in the last five years, according to a new Tearsheet report. 

One of the key reasons for those lackluster results may be a culture of fear versus trust, and stagnation versus reorientation. 

After all, only 43% of execs clearly communicate to employees that unsuccessful experimentation will not adversely impact their career or compensation, according to Tearsheet. In other words, banks need to learn to fail faster.  

Teams from across the bank need to work together and be aligned on positive incentives, not negative ones. Beyond the nuts-and-bolts of new technology, that requires a mindset of experimentation, teamwork, and trust. As former Credit Suisse CIO Radhika Venkatraman previously put it to Insights Distilled: “If you want to disrupt yourself digitally, you must reimagine your data, operations, talent, and culture.”

Summed up another way by Tearsheet’s Rabab Ahsan: “A successful transformation is less about jumping on technological bandwagons and more about ensuring that clear roadmaps, communication across teams, and incentivization for everyone are in place.” 

A perfect storm of conditions could make technology M&A more appealing than ever for big FinServs, who may view it as a way to protect their operations and could benefit from more-attractive terms. 

Experts were already predicting that 2023 would likely be a big year for tech M&A for banks, and the aftershocks of Silicon Valley Bank’s collapse may create even more incentives.  

When SVB’s implosion threatened many software startups that relied on it, FinServ CIOs had to grapple with whether they had backstops for all the providers in their technology supply chains.  

This stressful process could have execs eyeing acquisitions to safeguard the tech that has become integral to their infrastructure, Forrester analyst Stephanie Balaouras told The Wall Street Journal. 

Or, as CIO of Nutanix Wendy Pfeiffer mused: “Are there opportunities for me to maybe invest in a partner and make sure that they’re there for me?”  

Meanwhile, the current economic climate and tough funding environment could lead to better terms for acquisition-hungry banks and insurance firms.   

Eager acquirers should remember, however, that careful due diligence and a specialized approach to retaining talent are crucial to making a purchase successful instead of a flop, as Bain analysts previously told Insights Distilled.  

Neobanks have touted their account-opening ease and digital experiences as factors behind their surge of new clients, underscoring traditional banks’ need to continue revamping their processes. 

Money is moving right now and having a seamless customer experience is a ticket to winning it.

Neobanks and fintechs like Mercury, Meow, NorthOne, and Arc have capitalized on SVB’s failure with a surge of new clients, and experts attribute it to their digital acumen and fast account-opening processes. 

For example, neobanks can open accounts for new clients online in under an hour, while traditional banks may take days. They also have a “great digital-only experience,” as Alloy Labs CEO Jason Henrichs put it to American Banker.  

There’s also their flexibility to launch new promotional campaigns or product strategies. For example, Mercury quickly boosted its FDIC insurance coverage to $3 million last week by splitting up customer funds across a network of different bank partners.  

Money is moving right now and having a seamless customer experience is a ticket to winning it.  

While signs point to the fact that traditional institutions ultimately stand to be the biggest beneficiaries of SVB’s collapse (Bank of America, for example, has reportedly gained $15 billion in recent deposits), they can still learn from their fast-moving counterparts. In short, the need to simplify account openings and improve digital capabilities is more important than ever.  

ChatGPT – the AI-powered tool that can respond to prompts and queries in a human-like way – can help make vast proprietary datasets more accessible: Advisors can use it to access key information, quickly. 

Morgan Stanley Wealth Management is using GPT-4 – the newest version of the buzzy chat tool – to drive efficiency and competitive advantage for its financial advisors.  

The bot will source its information exclusively from MSWM content: The firm trained ChatGPT on about 100,000 pieces of its own proprietary research. Querying it will allow advisors to receive answers in seconds and more easily digest large amounts of data without manually combing through reports.  

This allows them to use Morgan Stanley’s own insights in new, richer ways, free up time, and better serve clients.  

“It will be like having our chief investment strategist, chief global economist, and global equities strategist on call for every financial advisor 24/7,” the head of analytics, data, and innovation, Jeff McMillan, said of the tool.   

The feature essentially gives advisors superpowers that amp up versus minimize their ability to have personal, trusting relationships with their clients.  

Morgan Stanley’s sanctioned use of ChatGPT resembles that of Swedish investment firm EQT, which programmed the chatbot to help its dealmakers more easily benefit from its “Motherbrain” data platform. We expect to see more FinServs proactively rolling out ways for employees to use this tool, though for the time being many big banks have banned work usage of ChatGPT, until it’s properly vetted. 

As the number and diversity of cyberattacks continue to increase, FinServs need to use sophisticated tech tools to outsmart their enemies – including the use of “honeypots” to gather data. 

Mastercard announced the acquisition of Sweden-based cybersecurity firm Baffin Bay Networks on Monday to help its clients fend off cyberattacks.  

Baffin Bay’s threat protection platform uses predictive, AI-based technology to automatically filter and counteract malicious internet traffic, while gathering data about bad actors. As distributed denial of service (DDoS) attacks continue to increase, there’s a greater need than ever for a strong line of defense against breaches.  

“On a technical level, Baffin Bay Networks leverages sophisticated AI modelling to distinguish between good and bad traffic,” a spokesperson told Insights Distilled. “It operates a large network of decoy systems that security teams can use to spot attacks early and gather information on attacker tools, tactics, and procedures (these are called ‘honeypots’).” 

The acquisition aligns with Mastercard’s strategy of offering additional services beyond payment transactions.  

“The solution complements our existing vulnerability diagnostics, equipping customers to take a targeted next step in protecting their cyber environment,” according to Mastercard’s president of cyber and intelligence, Ajay Bhalla. Neither firm disclosed the terms of the deal.  

The announcement closely follows Mastercard’s news that it’s experimenting with ways to use quantum computing to improve its loyalty and rewards program. 

Through continuous monitoring and cost transparency, Liberty Mutual has reduced its annual projected cloud expenses by 20% – and it aims to achieve 25% in 2024.  

Liberty Mutual now has about 70% of its IT assets in the cloud, and has been incredibly deliberate in how it has shifted its workloads.  

“We actually have a rationalization strategy where we’re shutting down 100 to 200 systems a year,” Liberty Mutual CIO Monica Caldas told CIO Dive in a recent webinar. “And we’re keeping that close and monitoring it to make sure that we actually do the stewardship part.”  

That’s necessary to avoid the “tough spot” of just lifting-and-shifting, she said: Cloud computing can get expensive, so CIOs need a well-defined plan to manage costs. Rationalization – the process of determining the best way to migrate or modernize apps in the cloud – has been crucial to the insurance firm’s transformation, she said. 

Cost management starts with cost transparency: For example, Liberty Mutual displays costs in its developer consoles, so teams can directly see how their spending has fluctuated from month to month. (For more cloud cost-mitigation advice from experts, check out this recent edition of Insights Distilled.) 

Saving money on cloud costs can ultimately create a “flywheel effect,” Caldas says, where the budget gets funneled into other innovation projects.  

Tech like ChatGPT – the AI-powered tool that can respond to prompts and queries in a human-like way – is helping Goldman’s developers get their work done, according to an exec. In some test cases, they’ve used it to write as much as 40% of their code automatically. 

Goldman Sachs is allowing its engineers to use a generative AI tool to write code more efficiently, according to chief information officer Marco Argenti.  

Developers can use the technology to both test existing code and generate new work, Argenti told CNBC, allowing them to be more productive. Generative AI for engineering – like GitHub Copilot – can also enhance creativity by eliminating rote work and helping users get past questions they’re stuck on.  

“If you actually have a GPT-like technology that tests the code, or you generate the tests for the GPT code, you’re creating this dualism where you test the machine and you get the machine to test your work,” Argenti said.  

He declined to specify which generative AI products, specifically, Goldman has used and stressed that the firm’s deployment of the tool is still in “proof of concept” mode. But its experimentation is a crucial step to understanding the tech’s potential: “I’ve been in technology probably almost four decades or so, and this is one of the biggest disruptions I’ve ever seen.” 

Meanwhile, Morgan Stanley is giving its financial advisors access to ChatGPT to synthesize its proprietary research, Swedish investment firm EQT programmed a chatbot to help its dealmakers benefit from its “Motherbrain” data platform, and fintech Klarna has deployed it externally for shopping recommendations.  

We expect to see more FinServs proactively rolling out ways for employees to use this tool, though for the time being, many big banks have banned work usage of ChatGPT, until it’s properly vetted.