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Artificial Intelligence Trends in Focus at William Blair Client Gathering in New York

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Deeper Relationships Require Intelligence: Companies Need New Technologies to Connect the Dots in Customer Data

If you track all of the emails that your marketing and sales reps send, all of the phone calls they make, and all of the meetings they schedule, you might think you have everything you need to get a complete picture of the relationships they have with customers and potential customers.

Relationship intelligence, a new type of technology that combines data collection, artificial intelligence, and analytics, will allow you to connect all the dots to ultimately make more informed decisions and to provide the appropriate coaching where necessary.

'Organizations are increasingly investing in a data lake approach that aggregates incredibly large volumes of transaction data, customer information, market intelligence, and activity signals that, when taken together, paint a highly detailed and actionable picture of the state of buyers,' says Geoff Webb, vice president of product marketing at PROS, a digital commerce solutions provider.

'Machine learning-based models, tuned and optimized by AI engines, can now evaluate and look for underlying patterns and trends in massive quantities of data, and they can do it fast enough to keep pace with the speed of digital commerce.

By enabling all parts of the organization to have a common perspective and view of customers and their behavior, they have greater alignment and can better focus their resources toward delivering the common goal of a great customer experience, according to Webb.

Artificial Intelligence Applications for Lending and Loan Management

Lending is a massive business in the United States which directly and indirectly touches almost all parts of the economy.

With tens of millions of Americans holding loans worth trillions of dollars, any technology that can make even a small improvement in a company’s returns on the loans they hold, or that can improve their share of the market, would be worth a significant amount of money.

The value of a loan is thus tied to assessments of the value of the collateral (car, home, business, artwork, etc…), the likely level of future inflation, and predictions about overall economic growth.

(Readers interested in a broader perspective on finance applications beyond loans can explore our full article on finance applications of AI.) The value of most loans is mainly based on how likely an individual or business is to pay it back, so determining how likely an individual is to default is critical for the whole sector.

While in the past lenders looked at only a few metrics like FICO score and income, companies have started looking at an individual’s entire life and even their vast digital footprint to determine how likely they are to default.

The idea is that extra data provides not just more insight into people with established FICO scores, but that it can be particularly useful for determining the creditworthiness of people without a traditional credit history.

The company started in 2011 and focuses on emerging markets where rising middle classes often lack traditional credit histories or even bank accounts.

ZestFinance will use Baidu search data to develop credit scores for individuals, giving them a massive amount of data for the large Chinese market where traditional credit score systems are mostly lacking.

Peter Maynard, Senior Vice President of Global Analytics at Equifax, in interview this year claimed their new “neural network improved the predictive ability of the model by up to 15 percent.” Using it to look back at recent decisions, they found that loans which were turned down could have been made safely.

In addition to traditional FICO scores and years of credit, Upstart also takes into account education, SAT scores, GPA, field of study, and job history to use machine learning to predict an individual’s creditworthiness.

While other companies have also automated some of the data entry, processing of paperwork, and verifying basic information most loan applications are still reviewed by a human underwriter before they can be approved.

Obviously, big banks along with a whole range of sectors are adopting general AI-powered tools such as chatbots, customer relationship management tools, and advertising analytics.

They claim their system uses machine learning to determine analyze individual’s financial habits to determine if they can afford to pay back their student loans more quickly.

Given the dominant role Amazon plays in online retail, it has a huge amount of proprietary information on what products are sold on their site, how customers feel about those products, the economic status of the companies which make those products, and the likely future demand for these products.

For example, a program might not deny applications from protected minorities,  but it might deny applications for individuals who have a dozen data markers that just happen to highly correlate with those groups.

There are billions of people without real credit histories that companies may one day want to offer mortgages, payment plans for products, credit cards, or other loans.

Even if machine learning can accurately use an individual’s digital footprint (purchase history, app use, search history, social media activity, etc) to determine their creditworthiness, it doesn’t necessarily that machine learning systems will always yield better results than traditional credit measures (though even those traditional methods for credit analysis may be low-hanging fruit for “white collar automation”).

As we saw with the crash of the credit default swap market, if the incentives are wrong, companies can create reasonable sounding justifications to give bad loans the seal of approval.

IBM Sep 2020 105.000 put

The source selection process was conducted in accordance with the stated criteria in the solicitation and procurement law.”Representatives of Amazon didn’t immediately respond to requests for comment on Friday night.Read More: Amazon Seen Focusing on Trump in Pentagon Contract ChallengeIn court paperwork, Amazon said the lawsuit was filed under seal because it contains “sensitive” information that “would cause severe competitive harm” if it’s released.Political InfluenceMicrosoft asked to intervene in the suit, arguing in a filing that the case will “directly affect Microsoft’s interests” and “carries the potential for significant economic and other harms to Microsoft.”It’s widely expected that Amazon will argue that its loss of the high-profile contract was due to improper political influence by President Donald Trump.

Mattis has criticized the book, but hasn’t commented on the allegation concerning Amazon.Dana Deasy, the Pentagon’s chief information officer, said during his confirmation hearing in late October that to the best of his knowledge, no one from the White House reached out to any members of the JEDI cloud contract selection team.Read More: Trump Attack on Cloud Bidding Gives Pentagon Chief Hard ChoicesProcurement experts have said it’s difficult for losing bidders such as Amazon to overturn contract awards by alleging political or vendor bias because it requires the company to show the agency took Trump’s comments into consideration when it picked Microsoft.While no law prohibits a president from weighing in on a contract, federal agencies must follow strict rules about what they can and can’t consider when making an award decision.

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