Artificial Intelligence Solutions for Banking
Artificial Intelligence Solutions for Banking
Though banks don’t create AI strategies, they are increasingly using artificial intelligence and machine learning in their day-to-day business. We frequently work with them on ideation workshops, PoC, and solution implementation. Santander Consumer Bank, for example, is running workshops and researching how to use machine learning to boost the sustainability of loan portfolios.
Besides credit risk modeling, there is already an impressive range of use cases for AI in banking. It covers everything, from customer service to back-office operations. The most common AI solutions in the banking sector are listed below:
Customer service automation
Chatbots
Applying chatbots to automate customer service helps customers to satisfy. Moreover, simple issues can be solved entirely without human interference. In other words, automation significantly reduces customer service workloads.
Biometric identification
It enables detailed or unnoticed identity verification within remote channels. It can include voice identity verification in call centers or typing verification in online banking.
Customer insights
Customer 360 view
Using deep learning to customer analytics makes it easier to combine insights from various data sources such as transactions and online banking logs. It helps to understand a bank’s customers better and create personalized recommendations and intelligent customer assistants, making the business more responsive and efficient.
Churn prediction
Because of accurate AI algorithms, churn probability predictions improve customer retention. This is crucial as customers frequently stir without obvious warning signs. Therefore, it is challenging to run mainly targeted anti-churn campaigns. On the other side, retention activities can be costly, sometimes much more so than the value a potential customer may bring.
Customer lifetime value
Customers’ lifetime value is often used to analyze how valuable a particular relation is and to optimize other activities, such as by integrating customer lifetime value with a possibility-of-churn function to focus retention activities on the most valuable clients.
Stimulating Sales
New client acquisition
Deep learning is focused on improving remarketing. With the 360 customer view, it promotes the use of all possible information about a customer. It includes cookies and how the person has communicated with a website. Understanding customer behavior on the internet enables a bank to focus on marketing activities on potential customers. It also shows them personalized ads, translating into even a 25x increase from advertising activities.
X-sell
Machine learning methods can be used to improve the selection of customers targeted for outbound CRM campaigns. They collect and combine the benefits from both the customer 360 view and the advanced probability of purchase predictions. It allows a bank to choose the right customer and the right product to cross-sell. For example, machine learning has been shown to improve credit card x-sell by 12.5%.
Credit risk management
Loan application assessment
Machine learning can process unstructured data like transaction descriptions more thoroughly than other techniques and discover non-obvious dependencies. Machine learning methods can also be combined with traditional scoring models to get better results.
Fraud Detection
Machine learning thoroughly detects frauds, adapting to individual patterns, and changing behaviors. It can be used in areas where a high volume of events requires to be analyzed in real-time. AI can spot complex correlations; hence the wildest purchase will make sense to AI. Those who cannot wrap their algorithms will drive to the detection of fraud.
Debt collection strategies
Algorithms of AI can generate a customized communication strategy for each customer. Recommending script for CC, or purpose a schedule, it will adjust the contact channel.
Constant portfolio evaluation
Identifying SME clients enables banks to react rapidly and start the recovery process before other creditors do.
Back-office optimization
Workflow documentation
Categorizing incoming emails to visit the appropriate department such as sales, and complaints and customer segmentation like an individual, SME, Corporate reduces the work manually involved with organizing customer service departments.
Process automation
It includes cash operations, trade finance, and credit application processing, and accounting processes.
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Published at Fri, 25 Sep 2020 12:00:00 +0000
Amesite Inc. Announces Pricing of Initial Public Offering
Will Trade on the Nasdaq Capital Market Under Ticker “AMST”
ANN ARBOR, Mich., Sept. 25, 2020 (GLOBE NEWSWIRE) — Amesite Inc. (the “Company”), an artificial intelligence software company providing online educational programs for higher education, announced today the pricing of its initial public offering of 3,000,000 shares of common stock at an offering price of $5.00 per share. In addition, Amesite has granted the underwriters a 45-day option to purchase up to an additional 450,000 shares of common stock at the public offering price per share, less the underwriting discounts and commissions, to cover over-allotments, if any.
The shares of the Company’s common stock will begin trading on the Nasdaq Capital Market on September 25, 2020 under the ticker symbol “AMST”.
Laidlaw & Company (UK) Ltd. acted as the sole book-running manager for the offering. The Benchmark Company, LLC acted as the co-manager for the offering.
The offering is being made solely by means of a prospectus. A copy of the final prospectus related to the offering may be obtained, when available, from Laidlaw & Company (UK) Ltd., 521 Fifth Ave., 12th Floor, New York, NY 10175, telephone: (212) 953-4917 or email: syndicate@laidlawltd.com. Investors may also obtain these documents at no cost by visiting the Securities and Exchange Commission’s (“SEC”) website at http://www.sec.gov. Before you invest, you should read the prospectus and other documents the Company has filed or will file with the SEC for more complete information about the Company and the offering.
A registration statement on Form S-1 (File No. 333-248001) relating to this offering was filed with the SEC and was declared effective by the SEC on September 24, 2020. This press release does not constitute an offer to sell or a solicitation of an offer to buy the securities described herein, nor shall there be any sale of these securities in any state or jurisdiction in which such an offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction.
About Amesite Inc.
Amesite is a high tech artificial intelligence software company offering a cloud-based platform for college, university and business learning products to be cost-effectively and conveniently delivered to learners online and in hybrid online/on campus formats. Amesite uses artificial intelligence technologies to provide customized environments for learners, easier-to-manage interfaces for instructors, and greater accessibility for learners in the US education market and beyond. The Company leverages existing institutional infrastructures, adding mass customization and cutting-edge technology to provide cost-effective, accessible, scalable and improved experiences to learner. For more information, visit https://amesite.com.
Forward Looking Statements
This communication contains forward-looking statements (including within the meaning of Section 21E of the Securities Exchange Act of 1934, as amended, and Section 27A of the Securities Act of 1933, as amended) concerning the Company, the Company’s planned online machine learning platform, the Company’s business plans, any future commercialization of the Company’s online learning solutions, potential customers, business objectives and other matters. Forward-looking statements generally include statements that are predictive in nature and depend upon or refer to future events or conditions, and include words such as “may,” “will,” “should,” “would,” “expect,” “plan,” “believe,” “intend,” “look forward,” and other similar expressions among others. Statements that are not historical facts are forward-looking statements. Forward-looking statements are based on current beliefs and assumptions that are subject to risks and uncertainties and are not guarantees of future performance. Actual results could differ materially from those contained in any forward-looking statement. Risks facing the Company and its planned platform are set forth in the Company’s filings with the SEC. Except as required by applicable law, the Company undertakes no obligation to revise or update any forward-looking statement, or to make any other forward-looking statements, whether as a result of new information, future events or otherwise.
Contacts
Media Contact:
Robert Busweiler
Busweiler@sunshinesachs.com
631.379.6454
Published at Fri, 25 Sep 2020 12:00:00 +0000
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