Artificial Intelligence or AI is undergoing a resurgence after a winter of disillusionment in the nineties and early two thousands. This revival is well-demonstrated in the financial services industry. As early adopters of emerging technologies, banks and other financial service providers are rapidly embracing AI, even though its capabilities are still limited to narrow applications.
As a result of this adoption, Autonomous forecasts upwards of $1 trillion in savings to the industry. Leading this wave of AI in finance are fintechs, most of which are working directly or indirectly with financial industry players to create real-world AI applications. This list highlights ten such AI for finance startups.
Signifyd works with e-commerce companies to help streamline and speed up the approval process at checkout while reducing fraud and increasing compliance. By combining data from a network of over ten thousand merchants, the startup is able to generate customer risk profiles.
When applied at checkout, these solutions can help e-commerce merchants significantly reduce fraud cases. Signifyd currently works with companies like Jet.com, Lacoste, and Build.com, while its software integrates with Shopify, Magento, Big Commerce, and Salesforce Commerce Cloud. The startup, headquartered in San Jose, California, USA, has raised $206 million to date.
Founded in 2013, DataVisor helps banks and other financial institutions combat fraud and financial crimes using AI. The startup, based in Mountain View, California, uses unsupervised machine learning to identify fraud and financial crime campaigns well before they inflict significant harm.
It does this by applying advanced machine-learning techniques to data collected from over 4 billion financial services users across the globe. Where traditional fraud detection solutions are reactive, DataVisor offers a predictive self-learning financial security solution. The company has raised $54 million to date in funding rounds led by Sequoia Capital, Genesis Capital, New Enterprise Associates, and GRS Ventures.
Document processing, pitting human productivity against compliance issues, often serves as a bottleneck for back-office operations. HyperScience is solving challenges related to document processing through machine learning.
Founded in 2014, the New York-based AI startup has created machine-learning models able to automate data entry teams and increase the speed of processing invoices while maintaining high levels of compliance-driven information reconciliation. The early-stage company, which focuses on a narrow AI use case of processing structured and semi-structured documents, has so far attracted $48.9 million in funding.
Founded in 2012, AppZen automates back-office audit and compliance workflows using AI. The AI platform uses human-like intelligence and reasoning to fully audit expense reports, invoices and contracts, identifying discrepancies within seconds.
To train its AI, AppZen combines data from internal as well as external sources such as social media and third-party expense reporting apps like Oracle, NetSuite and Concur. AppZen’s audit and compliance AI uses advanced computer vision and natural language processing (NLP) as foundational technologies. The company has raised $52.6 million to date and counts Amazon, Hitachi, Novartis, and Airbus as customers.
ZestFinance is using AI to help financial service providers carry out better risk profiling and credit modeling. The AI finance startup, which focuses on credit underwriting, is helping banks and other financial institutions increase credit approval rates while maintaining or reducing defaults.
The startup’s proprietary service, Zest Automated Machine Learning or ZAML, is designed as a non-black-box AI solution that offers transparent explainability for all decisions made. Companies can opt to implement ZAML tools either on-premise or in the cloud. The company is headquartered in Los Angeles, California and has raised $268 million in venture capital to date.
Upstart is disrupting the lending market by using AI to enable a radically different type of credit scoring. While traditional lenders focus only on credit score and years of credit, Upstart uses additional data such as schools attended an area of study as well as jobs held in the past for credit profiling.
By using AI to process this data, the company can offer personalized credit to borrowers. Founded in 2012 by ex-Googlers, the company also offers its unique credit-scoring AI platform as a SaaS tool for banks, credit unions, and other financial service providers. The company has raised $584 million to date.
Cape Analytics leverages AI and geospatial imagery to help insurance companies better assess the risk and value of properties. The AI startup, founded in 2014, collects geospatial imagery data from dozens of sources and then uses AI to analyze and score properties within these images.
In this way, insurance companies can get instant property intelligence, which provides them with more data to include in underwriting modeling. By evaluating underwriting in this way, insurance companies can automate and streamline a currently tedious process. The company offers its services as either a SaaS solution or via an API that integrates with existing systems. To date, Cape Analytics has raised $31 million.
FutureAdvisor uses AI to automate wealth management and offer investment recommendations. By combining personal investment accounts like 401(k), IRA, and other taxable accounts, FutureAdvisor’s proprietary algorithm monitors the overall investment health of your portfolio, scanning for investment and tax-break opportunities.
With a team of chartered financial analysts and other experts on hand to help train and optimize the investment algorithms, FutureAdvisor offers a household-wide, long-term investment perspective to retail investors. The AI startup has attracted $21 million in funding from Sequoia Capital, Canvas Ventures, and others.
Wealthfront is a robo-advisor utilizing AI to offer exclusively software-based financial planning, investment management, and banking-related services. Targeting retail investors who may not have the skills or experience to invest, Wealthfront touts its service as a financial copilot to help customers achieve their financial goals.
By cutting out tedious investment calculations and numerous calls with brokerages, Wealthfront’s mission is to democratize investing and make it accessible to anyone. The company currently manages $12 billion in assets and has attracted $204 million in funding to date.
Ayasdi has built an AI-as-a-Service platform that helps financial service providers and other enterprises deploy AI solutions against the challenges they face. As most enterprises are still experimenting with AI, Ayasdi offers an AI sandbox that allows companies to try out AI applications without having to build the entire infrastructure in-house.
For example, Ayasdi works with Citi to enable internal and external users to find valuable insights from large and complex data sets. The company, founded in 2008 and based in Menlo Park, California, has so far raised $97 million in venture capital.
The Next Iteration of AI in Finance
AI in finance is a broad and rapidly evolving trend. One common thread among all these startups is that they are using AI to attack challenges the financial industry faces today. It is apparent, then, that AI is helping streamline current processes and introduce resultant efficiencies.
However, the next iteration of AI will see the introduction of completely novel services and solutions that disrupt current financial services. Such disruption may include the total elimination of fraud (upending fraud tracking services), instant credit (upending credit modeling services), autonomous and individualized financial advisors (upending financial advisory services) and others. In the coming years, expect to see more startups introducing these breakthrough AI applications in finance.
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