May 16

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.