editions

Spend management recently ranked above cybersecurity as the top challenge enterprises face with the cloud. The FinOps Foundation wants to make it easier to understand (and manage) expenses by creating a common data format for all cloud providers. 

Wrangling cloud costs across different vendors can be a struggle for large enterprises, because providers don’t use the same terminology, pricing structures, or billing formats.  

The FinOps Foundation wants to change that.  

The group launched a new project to create a standard language for cloud billing, led by Citigroup’s former head of global cloud financial management, Udam Dewaraja. Dubbed FOCUS, the specification will help companies demystify their spending across various public cloud providers – which could ultimately help them find ways to reduce costs.  

The initiative comes as enterprises have woken up to how sprawling cloud services can produce unexpectedly high bills. Nearly 45% of respondents in a survey published last fall said that at least one-third of their cloud spend is wasted each year, with lack of visibility into their usage as the main culprit.  

Banks have increasingly built their own tools and best practices for monitoring and optimizing budgets, but it’s difficult to compare billing data (and understand value) across cloud providers. FOCUS aims to make that spend management easier: If all providers speak the same language, then enterprises can benefit through greater understanding, analysis, and trust overall.   

Dewaraja will use his experiences from Citi to lead the group towards a “vendor-neutral, open-source specification” that will drive the financial operations discipline “forward in a major way.

GenZ and Millennials are interested in understanding their carbon footprints, so adding tracking tools can be a competitive differentiator for banks.

Westpac wants to help its customers make greener spending choices.  

The bank has partnered with Cogo to give its app users high-level insights into their carbon footprints, at a time when young people have expressed interest in having their banks’ help them better understand their climate impacts, according to a Cornerstone Advisors survey.   

Cogo estimates the carbon emissions of all a user’s transactions, and then pairs that carbon data with behavioral science to nudge them on ways they can reduce their personal carbon footprints. Users will also be able to see how their footprint changes month to month and compares with the Australian national average.   

The carbon footprint tracker “is a great conversation starter that encourages our customers to consider their carbon emissions,” said Westpac chief sustainability officer Siobhan Toohill. Meanwhile, the bank is on a path to bring its own net emissions to zero.  

NatWest, ING, Santander, and RBS have also worked with Cogo, while Standard Chartered and Bank of the West have partnered with carbon tracker Doconomy. 

By using an AI-powered bot to collect, combine, and summarize information, fraud investigators can more efficiently weed out false flags and compile reports for actual criminal issues.

Startup SymphonyAI just launched a new tool to aid financial firms’ criminal investigations. 

Big FinServs have started to inject artificial intelligence into outdated know-your-customer and anti-money laundering processes to cut down on manual work and catch more bad actors, and Symphony’s new tool takes those efforts one step further.  

The firm’s new “Sensa Copilot” allows investigators to ask questions when they get a new alert, tap all available context and data sources, receive risk summaries, and draft suspicious activity reports, if necessary. Initial users report that the tool reduces the time to resolve a financial crime investigation for new alerts by up to 70%.

The platform relies on Microsoft Azure’s OpenAI service combined with Symphony’s own large language models to analyze customer data sets and linked external sources. The firm is investing “aggressively and boldly in unlocking the power of generative AI when it’s coupled with rich, specialized domain models and data sources,” exec Mike Foster said.  

While the Copilot feature is still in beta testing, SymphonyAI counts Mizuho’s investing banking arm and Citi among the customers for its overall Sensa platform.  

For other examples of how the likes of Morgan Stanley and Goldman Sachs are taking advantage of the buzziest technology on the block, check out Insights Distilled’s past coverage on how generative AI is shaping financial services.   

There’s a growing trend within banks of appointing execs to specifically focus on internal tech experiences, as they realize that keeping top developers happy and efficient is key to building better products.

Bank of America’s long-time chief information security officer, Craig Froelich, just got a new job: He’s now the chief information officer of architecture, developer experience, and policy.  

This is a newly created position at BofA, according to Insider.   

Banks have known for a while that technology is key to remaining competitive, but there’s been a recent shift towards understanding how important developers’ internal tools and experiences are for building better customer-facing products. When devs have access to the latest tooling, they build better features.  

For example, JPMorgan appointed James Reid as CIO of a new employee experience and corporate technology org, Capital One named Peter Torres as its head of DevOps for its software org, and Goldman Sachs CIO Marco Argenti has often touted the importance of developer experience to achieving business goals.

Citadel, meanwhile, has a team of 20 engineers focused on making the investment firm’s tech tools easier to use. 

A corporate social network called Louisa aims to proactively connect employees within the same firm who might benefit from knowing each other.

What if every employee at your company had each other’s knowledge at their fingertips?  

That’s the world imagined by Louisa, which just spun out of Goldman Sachs to help employees at banks, VC firms, or other big companies better connect with their coworkers.  

The platform integrates data from HR records, CRM coverage lists, transaction histories, and more, to auto-populate profiles, create newsfeeds, and provide networking suggestions. Users can search the AI-enabled system by things like topic area or department. The idea is that workers can use the tool to build internal relationships that will ultimately help them win clients, complete deals, and ultimately “play as one firm.”  

“Think of Louisa as an AI-powered LinkedIn on steroids,” says Rohan Doctor, who founded the company within Goldman’s accelerator program back in 2018. The incubator program encourages employees with startup ideas to develop and build them in-house, and eventually launch them externally, hence Louisa’s debut.  

Early results seem promising: The network has 20,000 monthly active users, and several clients besides Goldman, including a commercial bank and a VC firm. By connecting coworkers, the platform can help companies reclaim “billions of dollars” otherwise lost to missed opportunities or bad client experiences. 

“Global organizations are competing to get the best return on their largest investment: their people,” said Doctor. “Louisa helps them maximize that return by unleashing an even greater strength: the collective intelligence of their entire workforce.”   

While some say that the best way to stamp out check fraud is to eliminate physical checks, banks also need to step up their know-your-customer protections for depositors by taking advantage of the latest fraud-busting technology.

Check fraud has spiked dramatically in the past 18 months, harming both consumers and banks, according to a recent Wall Street Journal report.  

For years, check fraud declined in the US, but this old-fashioned crime started spiking again during the pandemic. US banks filed 680,000 check-fraud reports in 2022, which is almost double the number filed in 2021, according to the Financial Crimes Enforcement Network. And the WSJ reports that it can currently take weeks or even months for banks to investigate check fraud claims, indicating they need to better coordinate and automate their investigations. 

To get to the root of the problem, some experts say that phasing out physical checks is the best path forward. But perhaps more importantly, banks can also do a better job preventing bad actors from ever being able to open “mule” accounts, where stolen and altered checks are often deposited. 

A trade group for community banks called out a handful of big FinServs earlier this year, insisting that they needed to do a better job resolving check fraud claims by beefing up their know-your-customer (KYC) practices. “The largest banks are enabling a weak link in this crime chain by permitting fraudulent accounts to be opened in the first place,” a leader of the trade group said at the time. 

Customer onboarding and KYC checks have long required manual processes, paperwork, and legacy systems, but Insights Distilled has previously reported how new, improved technology is now available. In short, banks need to upgrade their practices. 

For example, perpetual KYC uses AI-powered data crunching to transform it from “an activity that occurs irregularly every few years after onboarding to an automated trigger-based activity that works in real time,” according to a Moody’s report from earlier this year.  

A host of startups offer AI tools to strengthen banks’ know-your-customer protections, including Citi Ventures-backed Quantifind, SymphonyAI, and Chekk. To protect the ecosystem as a whole, banks need to explore their options to find a solution that works better for customers and themselves.  

As the director of the Consumer Financial Protection Bureau urges lenders to look beyond three-digit credit scores, Prism Data says that its cashflow-tracking tech can help incumbents serve more customers, with less risk.

There’s a new startup on the market to help FinServs make better lending decisions through cashflow-based underwriting.  

Prism Data – which uses bank transaction data to form a more complete picture of someone’s financial health and creditworthiness – just spun out of fintech Petal. The firms raised $35 million in fresh funding to be divided between them. 

“People’s bank account data captures many vital data points that don’t ever show up in traditional credit bureau data,” Prism’s head of communications, Matt Graves, told Insights Distilled, including monthly bills and BNPL loans. “Those data points are incredibly important to understanding whether a consumer can afford to take on a loan or a line of credit. But they’re not captured in a credit score.” 

The firm says that its API-driven solution is launching just as lenders are tightening standards and the Consumer Financial Protection Bureau (CFPB) is pushing open banking, with regulation proposals expected later this year.  

“Big banks and other lenders will need partners like Prism Data who can help them analyze consumer-authorized open banking data and use it to make better decisions and minimize credit risk,” Graves said.

Prism says that some of the largest banks in the US are already its partners or are currently conducting pilots with its technology, though it declined to name names. There are a handful of other firms working with FinServs to improve lending by combining more data and better modeling, including Insight Partners’ portfolio company Zest AI, which targets credit unions, Truist Ventures-backed Stratyfy, and Conductiv. 

As financial services leaders begin to dabble with generative AI, they should calculate how “superhumanizing” staff in different divisions could lead to the greatest return on investment. 

There’s a staggering number of potential uses cases for generative AI, so execs need to be strategic about what they prioritize.  

At a recent conference, Goldman Sachs CIO Marco Argenti discussed his own mental model for deciding where to apply the technology within the bank.  

“Think about ‘superhumanizing’ your top people: They could be 10-20% more efficient in terms of the companies that they cover, the clients that they cover, the strategies that they come up with,” Argenti said at the FinTech Nexus event, according to Insider. “Then you can price that amplification, and that will give you a bit of an idea of the return on investment, and that in turn will allow you to prioritize where to invest.” 

It’s crucial to be strategic about which GenAI use cases to pursue, in part because the technology is already proving to be expensive.  

“Here’s a moment where every company and every CEO and every CIO needs to go through that mental model,” Argenti said, and calculate “who to superhumanize to get the highest yield.”  

As FinServs have learned with previous technologies, cost transparency and planning are key to successful transformations, and the sooner that firms start thinking about the ROI of GenAI, the sooner they can get it out of internal research labs and into production. 

Goldman itself has started giving its engineers access to GenAI to write code, and using it for data management, with the goal of spreading institutional knowledge. For other examples of how financial firms are putting generative AI to the test, check out Insights Distilled’s past coverage. And if you have heard about an interesting FinServ use case that we haven’t covered yet, reach out and let us know. 

Big institutions like BNP Paribas, Goldman Sachs, and S&P Global just announced their plans to launch a privacy-enabled, interoperable blockchain network for financial markets.  

A group of big FinServs has agreed that it’s better when they’re together, at least on the blockchain.  

More than two dozen participants are set to launch the Canton Network this summer, according to a recent announcement, including a handful of financial institutions. It’s a “network-of-networks” in that it connects and makes interoperable any application built with Daml, a smart-contract language created by Digital Asset. 

The blockchain-based network captures “the benefits of public blockchains, without the flaws,” the group says, by balancing decentralization with privacy and interoperability.  

Public, layer 1 blockchains require full transparency, while private blockchains lose interoperability, a spokesperson told Distilled. Its solution allows highly regulated financial firms to run their own applications, with their own permissions, while being able to connect with other apps: “The Canton Network offers a third option apart from the public or private blockchain boxes. That is the North Star we are trying to provide for everybody.” 

After all, FinServs have realized that the interoperability and synchronization of their efforts is key to success, and the heyday of their widespread blockchain experimentation has given way to more focused, consolidated efforts.

The Canton Network can be used for the issue and settlement of digital bonds or other tokenized assets, while ubiquitous bank-to-bank messaging platform Swift recently trialed a blockchain-based application with six other securities participants, including Citi and Northern Trust, for communicating corporate events, like tender offers or stock splits.  

None of the biggest banks in the United States have the same organizational arrangement for their AI efforts; seeing other approaches – and getting to know some of the top execs – could inspire your strategy.

Who are the top artificial intelligence-focused execs in the banking world, and how do your peers and competitors structure their AI efforts?  

The top US banks each have their own unique strategies for deploying AI throughout their orgs, according to Insider. Here’s a peek at how several firms are taking on this transformational technology: 

• JPMorgan divides its efforts between Manuela Veloso, head of AI research, and David Castillo, firmwide head of AI/ML technology. Veloso oversees big-picture research, while Castillo leads implementation across JPMorgan’s operations. Drew Cukor, the firm’s chief data officer, also manages AI/ML transformation and engagement across the firm.  
• All of Citigroup’s artificial intelligence efforts funnel through its AI center of excellence, run by Prag Sharma. Sharma reports into Nimrod Barak, head of Citi’s Innovation Labs. 

• Bank of America organizes its AI efforts along business lines, with chief experience officers from difference divisions, including consumer banking (Teron Douglas) and wealth management (Christian Kitchell), reporting into BofA’s chief digital and chief marketing officer, David Tyrie.  

For more details and the structures at other top banks, read Insider’s full feature here.