editions

JPMorgan wants to deepen its relationships with venture capital investors and their portfolio companies, allowing it to offer them services “from inception through to IPO.” 

JPMorgan wants to be a one-stop shop for private companies and their investors.  

The bank is buying Aumni, a Utah-based analytics startup that helps its VC users replace Excel by making it easy to track and analyze their holdings in a given company.  

JPMorgan’s purchase follows the collapse of Silicon Valley Bank, which also catered to the startup world, though its relationship with Aumni stems back to 2021, when JPMorgan led the firm’s $50 million funding round.  

Aumni will be integrated into Capital Connect, the platform JPMorgan launched late last year that simplifies the funding process for startups and strengthens JPMorgan’s connections with young venture-backed companies.  

All told, JPMorgan has made dozens of deals since 2020, with a particular focus on consumer-facing technology or fintech. 

Biometric payments offer a convenient, secure way to make purchases. JPMorgan is testing the technology – which Goode Intelligence expects to account for nearly $6 trillion in transactions by 2026 – as a means of staying ahead of the payments revolution.

For years, financial institutions have allowed users to sign into their smartphone apps with facial recognition or a fingerprint. Now, JPMorgan is taking that vision of a cardless, passwordless future one step further into brick-and-mortar retail.  

Soon, the bank will enable merchants to offer customers the ability to pay for goods and services in-store using palm or face identification.  

JPMorgan’s “commerce solutions” division is launching its test run with brick-and-mortar establishments in the US this year, including Formula 1 race Miami Grand Prix. It’s deploying a combination of in-house technology and partnerships to launch the pilot and, if successful, plans to use its initial learnings to launch the service more broadly in 2024.  

Biometric payments are seen as a frictionless, efficient option for customers to buy things – after all, there’s also no risk of leaving your payments method at home. 

“At its heart, biometrics-based payments empowers our merchant clients to deliver a better customer payment experience,” according to JPMorgan’s head of omnichannel solutions, Jean-Marc Thienpont.    

Similarly, Amazon and sandwich chain Panera just announced their own pilot for palm-scanning payments initiatives.  

Merrill is betting that helping people connect with advisors based on their preferences will help it hook younger, more digital-centric customers – and build its brand.  

Bank of America’s Merrill Lynch is rethinking relationship building for its financial advisors. 

While clients historically find their advisors through referrals, the wire house has launched a matching algorithm that can pair them based on their stated preferences. Potential clients answer questions about their investing approach, financial needs, communication style, and personality, and then Merrill will automatically recommend at least five advisors that its algorithm has highlighted as a good fit.  

The speed and experience mimic the format of popular recommendation or matchmaking sites and cater to a younger, tech-centric set of clients that want to quickly connect with a representative that suits them. 

This algorithmically driven recommendation is meant to be a “warm experience” opposed to a cold lead, Merrill’s head of platforms and capabilities, Casey Franz, told American Banker.  

Beyond making the process of finding an advisor easy for a new generation of digitally savvy clientele, the system helps Merrill build stronger relationships. The hope is that if a given financial advisor leaves the firm, the client will use the recommendation engine to find another manager, instead of following their original one outside of Merrill.  

The blockchain is well suited to corporate action processing, which has traditionally required significant manual effort and been rife with errors, because it creates transparency and traceability of data across a shared network. 

Ubiquitous bank-to-bank messaging platform Swift successfully trialed a blockchain-based application that it says could save the securities industry time and money.  

Working alongside six other securities participants – including Citi and Northern Trust – Swift trialed a new method of communicating corporate events (like tender offers, stock splits, and Dutch auctions) on an online blockchain. 

The new process simplifies an otherwise complex manual process of verifying a piece of news: “Our analysis found that asset managers often receive notifications from up to 100 different sources about the same corporate event, and the data is often different or contradictory from one source to another,” says Swift’s securities strategy director, Jonathan Ehrenfeld.  

Instead of asset managers needing to sift through all that information to verify data, the system automatically conducts peer-to-peer comparisons and creates a single, accurate shared document about a given corporate action. The process reduced errors, saved time, and could lower costs: Inefficiencies in communicating about corporate actions are currently costing each market participant $3 to $5 million a year on average. 

This is just the latest blockchain-related project to gain steam: A former Goldman Sachs trader recently launched a startup that’s using the blockchain to sell bonds. As Bain’s Thomas Olsen put it to Distilled recently: “Large financial institutions are seeing a window of opportunity” for Web3 and blockchain projects this year because of crypto’s problems. 

Working with fintechs can help financial institutions quickly launch innovative features and delight customers. While there’s no magic formula to crafting successful relationships, we’ve put together a playbook to help you build better partnerships. 

Big FinServs need to get better at partnering with startups. Upwards of 75% of banking leaders say they feel “pressured” to collaborate more with fintechs to meet consumer demand, according to a recent survey of 800 execs, but many are still “wrestling with the challenges” of doing so. 

“If you look at the industry at the moment, it can take 12-to-18 months for a bank to onboard a new supplier,” according to Oscar Brennan, the CRO of TechPassport, an organization created in collaboration with 15 global banks that aims to fast-track engagements between FinServs and startups. “As one fintech put it to me recently, ‘One of us is working in dog years, and one of us works in human years.’ There’s a real parallel.”  

To figure out how banks and insurance firms can speed up that process and work with ScaleUp more effectively and efficiently, Insights Distilled asked Brennan and six other leaders from the likes of Lloyds’ and Genesis Global about their guidance, expertise, and experiences.  

To learn what they told us, download the exclusive report here.  

Financial institutions often need to use fake data in product development to protect customer privacy – and generative AI makes building complex, realistic datasets much easier. 

A handful of big banks are getting real about fake data.  

UK-based startup Hazy just raised money from Wells Fargo, Nationwide Building Society, and Intesa Sanpaolo bank, among others, to simplify the previously time-consuming, expensive, and risky process of wrangling test data.  

“Teams now get realistic data in hours or days, rather than weeks or months,” according to Nationwide’s chief data officer.  

Synthetic data creation is another example of generative AI, which has become one of the hottest areas of investment and experimentation.  

While some financial institutions choose to generate their fake data independently, finding a dedicated partner can help save time and effort (Insight Partners’ portfolio company Tonic AI also has a slew of finance and insurance clients).  

To avoid losing GenZ (and GenAlpha) to neobanks and other digital upstarts, traditional banks need to find ways to build their brands with young consumers. Interactive financial literacy resources can help hook them early. 

Kid-focused fintech Greenlight just announced a new program that allows banks and credit unions to offer its suite of education products to their customers – and institutions like Morgan Stanley and Washington Federal have already signed on.  

“Neobanks are coming after Gen Z really aggressively,” and Greenlight’s resources give banks the ability to start building relationships with that generation “before they start getting bombarded,” Greenlight exec Matt Wolf told Banking Dive.  

The co-branded experience gives banks a low-lift way to integrate engaging, kid-focused content into their existing ecosystems.  

“With 42.9 million Gen Zers estimated to use mobile banking by 2025, there’s a big opportunity to build relationships with young people starting now,” Wolf added to Insights Distilled.  

Morgan Stanley will offer Greenlight’s suite of tool to its CashPlus brokerage clients for free, allowing them to “teach their children about the world of money,” according to exec Tom Stanmeyer.  

This launch extends Greenlight’s earlier dabbling into B2B: It launched a partnership with JPMorgan Chase in 2020 to power the bank’s debit card for kids.  

Other banks are experimenting with the power of interactive education, too. For example, Truist acquired gamified finance app The Long Game last year, and TD Bank has a virtual stock market game and a resource hub for kids called the Wow! Zone. 

The who’s who of the banking world has teamed up to build a fintech that replaces the syndicated loan market’s spreadsheets, fax machines, and phone calls with a real-time and transparent new digital service. 

A handful of top banks are ready to say goodbye to the inefficient and fragmented process of syndicated loan dealmaking, where settlement times can average more than 20 days.  

Deutsche Bank, Morgan Stanley, US Bancorp, and Wells Fargo just invested in and joined Versana, a digital data platform originally spearheaded by JPMorgan, Citi, Bank of America, and Credit Suisse last year.  

The project is an example of how traditional financial firms can team up to focus their collective energy, skills, and experience on building themselves a better solution – and preventing an independent fintech from disrupting the process instead. 

Versana centralizes corporate loan data, providing greater transparency and faster processing. Its new bank investors and clients mean Versana will now have more than 75% of US loan market deals on its platform. 

“With the global capital markets in a fairly volatile state at the moment, now is the time to ensure that agent banks, lenders, fund administrators, and trustees all have the needed transparency and digital tools to manage and drive the syndicated loan market forward,” says founding CEO Cynthia Sachs. 

Personalization is the key to people’s hearts: A new Bain report shows that people want their primary bank to use their data to tailor banking experiences – and that they’ll be more loyal customers if it happens. 

Highly personalized banking experiences win people over and stop them from churning to other financial providers, according to a new report from Bain, which surveyed 30,000 consumers.  

Nailing personalization is a multi-step, tech-infused process:  

Banks need to be able to understand and anticipate a customer’s needs, engage at the right time with the right content, and measure outcomes so that personalization can improve over time.  

For many banks, the biggest challenge is breaking through their own silos to scale the necessary capabilities and technology across their org, according to Bain partner Maureen Burns.  

“The best personalization utilizes all of a customer’s interactions across products so that at each touchpoint, the bank is providing the content or offer that the customer will most value,” she told Distilled.  “New capabilities, including generative AI for language and images, accelerate the possibilities and expectations. Traditional customer engagement has largely been based on single-campaign ROI’s with some loose processes to negotiate across different products. This transformation is the biggest challenge to banks providing personalization at scale.” 

As Distilled has covered before, banks are finding creative ways to use automation, artificial intelligence, and even quantum computing to personalize their offerings for clients. Bain’s study shows that getting those experiments right is crucial for customer loyalty.  

The report’s findings align with a proof-point from Bank of America, which just announced that over 10 million clients have used its personalized banking platform, Life Plan. 

BloombergGPT demonstrates how organizations with vast troves of data can create domain-specific generative AI products that can perform better than more general large language models. 

Bloomberg created a massive AI model that could – as one fintech expert put it – “replace the analyst.” 

The company released a research paper that detailed how it trained a new large language model (LLM) on what it describes as the “largest domain-specific dataset” out there. Its training data set included 700 billion “tokens” (read: word fragments), 363 billion of which were taken from Bloomberg’s own financial data (which includes Bloomberg Businessweek stories, TV transcripts, SEC filings, and more). It was augmented with a public data set. 

“We see tremendous value in having developed the first LLM focused on the financial domain,” said Bloomberg’s chief technology officer, Shawn Edwards. The team published test results that showed how BloombergGPT out-performed other general language models in answering queries related to finance.  

While the firm hasn’t launched any specific apps or products yet, BloombergGPT could eventually allow Terminal clients to quickly retrieve information about public companies or ask complicated, multi-part financial-analysis questions. It could also help Bloomberg journalists write headlines.  

As the generative AI rush continues in full force, financial firms are eager to find ways to disrupt their own processes and offerings.  

“Bloomberg is a great example of a domain-based model where an incumbent has just as much opportunity as a startup, because they can use their unique access to data or domain knowledge to codify their IP into these kinds of new intelligent models,” according to Insight Partners managing director Ganesh Bell. “Anyone who has access to that kind of data can do that.” 

For example, 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, Goldman Sachs is experimenting with ways to use generative AI to write code more efficiently, and fintech Klarna has deployed it externally for shopping recommendations.