CBANC Acquires Lendwell

CBANC, a professional networking platform for banks, acquired fellow Austin-based fintech Lendwell in a deal this week. Terms of the transaction were not disclosed.

“Our acquisition of Lendwell is the next step in our strategy of unlocking the power of cooperation and the collective purchasing power of thousands of financial institutions within the CBANC Network,” said Bryan Koontz, CBANC CEO. “The Lendwell platform will help our network members reduce the cost of lending operations while improving their ability to serve their customers.”

This is CBANC’s first acquisition, but as the company seeks to add value to its platform and give members more reasons to log on, today’s purchase may not be its last.

Lendwell, a mortgage settlement services company, helps credit unions and community banks with the mortgage settlement process, primarily with refinances, second mortgages, home equity loans, and home equity lines of credit. The company offers a range of products including property condition reports, appraisals, title insurance, closing services, and more to serve as a one-stop shop for loan settlement products.

Lendwell’s SaaS loan aggregation offering gives banks access to quality mortgage settlement services at cost savings that add up to 20%. As a result of the acquisition, CBANC’s 8,000 financial institution members will have access to the Lendwell platform at no cost.

Lendwell founders Gabe Flores and Mike DeBonis have joined CBANC as the GM of Lendwell and as SVP of Engineering, respectively.

Founded in 2009, CBANC serves as an online platform where financial institutions can connect to share ideas, get their questions answered, build their brand and reputation, and publish reviews and insights on new products and services.

CBANC’s Founder and former President Myers Dupuy demonstrated the network at FinovateFall 2011 in New York. Earlier this month, the company hired on Mike Snavely as its Chief Commercial Officer. CBANC has raised more than $7 million from Adams Street Partners.

In Subtle Shift, Facebook Now Wants to Be the Conduit Between Bank and Customer

Facebook, in a shift that has evolved slowly over time, now sees itself as a channel between traditional financial institutions and their customers, particularly considering the prevalence of mobile channels in people’s lives. Nine in 10 U.S. checking account customers already use mobile devices for at least some retail banking activities, according to Facebook’s recently …Read More

Feedzai and North American Bancard Partner to Fight Fraud

A new partnership between North American Bancard (NAB) and Feedzai will put machine learning to work to help merchants better manage risk and fight fraud. The two companies have collaborated to build a set of customized fraud prevention tools that leverage real-time data insights to identify suspicious patterns and transaction anomalies that often are the tell-tale signs of criminal activity online.

Saurabh Bajaj, Feedzai Head of Product, emphasized the importance of real-time technology in the fight against emergent fraud threats. He pointed out that while consumers and merchants alike have benefitted from the rise of digital technologies in e-commerce, these gains have come at a cost.

“Digital transformation across industries has been great for consumers, but also for increasingly sophisticated fraudsters looking for new ways to commit fraud,” Bajaj said. “That’s why we need a real-time AI engine that can secure transactions, using intelligence across all business channels.”

NAB Chief Risk Officer Jay Nadarajah highlighted the importance of maintaining a quality customer experience while improving the security of that experience. “Facilitating safe, secure, and fast transactions while eliminating friction in the underwriting and risk processes through technological advancements is at the core of what we’re trying to offer to merchants,” Nadarajah said. “These technological advancements drive efficiencies and speed in identifying fraudulent activity.”

Founded in 1992, North American Bancard is a payments technology company with more than 350,000 business customers in the U.S. and Canada. The Troy, Michigan-based firm offers payment processing and acceptance solutions including free EMV and NFC hardware, no long-term contracts, and acceptance of most major credit cards and PayPal at rates starting at 0.29%.

Feedzai demonstrated its Fraud Prevention platform at FinovateEurope 2014. Earlier this month, the company announced a partnership with Raiffeisen Bank International to use advanced machine learning to help the European bank fight fraud. In March, Aite Group named Feedzai Best in Class in the vendor market for fraud and anti-money laundering solutions.

With more than $76 million in funding, Feedzai includes Data Collective (DCVC), Sapphire Ventures, Citi Ventures, and Oak HC/FT among its investors. Nuno Sebastiao is CEO of the company, which is headquartered in San Mateo, California, and was founded in 2011.

In Subtle Shift, Facebook Now Wants to Be the Conduit Between Bank and Customer

Facebook, in a shift that has evolved slowly over time, now sees itself as a channel between traditional financial institutions and their customers, particularly considering the prevalence of mobile channels in people’s lives. Nine in 10 U.S. checking account customers already use mobile devices for at least some retail banking activities, according to Facebook’s recently …Read More

Bankers Acknowledge That They Need to Help Consumers on Financial Health, Report Finds

For banks, improving their customers’ financial health is not just good business. It is imperative. A new survey found that 69% of bank executives agree that helping customers improve their financial health can increase customer engagement and boost customer loyalty for banks. And yet financial institutions struggle with creating meaningful features and services, such as PFM …Read More

Banking Hurtles Toward Ethically Problematic Credit Decisioning

The paramount technology objective today in banking is the implementation of artificial intelligence to enhance customer experience, products and, in turn, financial results, for both consumers and financial institutions.

As artificial intelligence and its compatriot technology, machine-learning algorithms, take a more prominent role in financial services, the ethical challenges posed by these technologies advance.

The banking industry needs to confront these ethical challenges — before they rile consumers.

AI and ML use data to produce algorithmically generated analysis and solutions. Not surprising to readers of this blog, AI/ML is a key area of development for financial services today. At INV Fintech, Bank Innovation’s sister technology accelerator, approximately 17% of the applications for its spring class, which officially starts tomorrow, were from startups focusing on artificial intelligence solutions.

The issue is that AI/ML will increasingly open the banking industry to discriminatory practices, not the other way around, and despite convention wisdom. In short, AI can make redlining or reverse redlining child’s play in comparison.

Consider how AI in underwriting works. The FI takes an inordinately large data set — the larger, the better — creates an algorithm that analyzes that data for various criteria, and then produces an underwriting decision based on that analysis. There are obvious factors that a lender can avoid using in the underwriting decision, such as gender or race.

More Complicated

But it gets much more complicated. Say the lender imports social media data, a common intent among AI adherents. If there are two applicants that possess nearly identical characteristics when looking at common criteria, such as credit score and debt-to-income ratio, the deciding factor between which of these applicants gets funding could boil down to their social media data. And how is that determined? Is it possible that one applicant, because she lives on a particular street, is rejected for a loan, while the other, who lives in, say, a better part of town is approved? The answer is at least, possibly.

“It can get complicated,” said Prema Varadhan, a chief architect at Temenos, the banking technology company. “Which one to pick? There are layers that you don’t explicitly code. They lead to very valid ethical questions.”

Varadhan said that many in financial technology are starting to inject “explainability” into their AI models.

“If you are responsible for [producing a] credit score, there are models,” she said. “But replace that with a machine-learning model — it learns and behaves differently. But if it can’t explain the decision, [the lender] can’t rely on machine learning alone. ‘Explainability’ is hard. But this year we are trying to inject explainability into the models.”

In other words, Temenos is aiming to have models not just produce credit decisions, but to add the “why” of the decision into the credit algorithm. Elements of a credit decision are called “features” at Temenos, and some features are obvious, like gender. Other features are more subtle, and that’s what needs to be included in the machine learning algorithm, Varadhan said.

Feeding Bias

“Machine bias doesn’t come unconsciously,” she said. “Someone has to feed them. If you introduce the feature, you are giving that consideration to the machine. It will get biased to that. … Machine [learning algorithms] get biased in there because it is tolerated. You can’t say, ‘We don’t know why the model is skewed.’”

But are bankers – or regulators – confronting the potential pitfalls of machine-learning algorithms? The panacea of robotic process automation is such that there is a headlong rush to embrace algorithm-generated products and services. Yet, the more data that is included in credit decisioning, the more likely it is that discriminatory subtleties will determine who gets and does not get a loan. “Features” like income, geography, employment, and even relationship status all can become primary drivers of credit decisions – without the tacit knowledge of lenders. And that’s the problem: the plausible deniability does not make such discrimination OK.

Other than a few Medium posts, rarely are ethics a part of the fintech discourse today. There is far more attention paid to which startup is becoming a unicorn or how much revenue that new technology will engender for the financial services industry than whether technology is treating all consumers equally.

But AI has profound ethical factors that demand consideration. Of all 15 sectors tracked by Edelman, the publicity firm, financial services has the lowest percentage of trust among consumers at 57%, compared with 78% for technology. How much lower will that drop when ethics are violated by ML? Lest you think this is a far-off problem, the availability of ML algorithms is becoming commonplace for developers.

“It used to be that AI required geeky data scientists,” Varadhan said. “Not anymore. AI algorithms have become so commoditized, and a lot of it is coming from Driverless AI. A lot is open source, so it comes at little cost.”

All this points to ML-driven advances coming at a faster speed. It is time the considerations of their ethical implications speed up, too.

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FinovateSpring Sneak Peek: Faraday

A look at the companies demoing live at FinovateSpring on May 8 through 10, 2019 in San Francisco, California. Register today and save your spot.

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Cory Albert, Account Executive
Albert has a strong background in strategic software and marketing technologies. At Faraday, he helps clients implement custom AI capabilities to exceed their goals.

Riley Dickie, Account Executive
Dickie has worked with Faraday’s consumer finance clients for the past three years, helping them take full advantage of their customer data with AI.