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Top AI Companies That Help Finance Companies To Upgrade

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Artificial Intelligence (AI) in finance is switching the traditional mode of operations into automation. AI, along with Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) is stretching arms across the financial industry.

Artificial intelligence in finance and banking offers many benefits to service providers. AI solutions for finance ensures affordable financial decisions at the right time. They also reduce the risk of fraudulent acts.

It is a primary reason why finance, insurance, and banking firms have deployed AI in their processes recently. So, rustic banking or finance service are becoming more modern with AI.

In this article, we tried to give detailed information on significant applications of AI for finance. Let’s start!

Uses of AI in Financial Services

 Banking and finance is a domain where hackers try to get confidential clients’ data and hack credit systems. Artificial intelligence companies are developing the most advanced AI-enabled financial solutions that protect a banking domain from hackers.

The applications of artificial intelligence in banking and financial services have great potential. They deal with risk management, asset management, customer support, and many more. Likewise, there are many more benefits of using AI in finance.

Moreover, with predictive analytics, AI-based finance services also spot longer-term trends. Thus, AI for finance helps bankers run businesses with higher productivity and efficiency.

USM has a bag full of specific AI applications for finance and banking companies. Our AI-powered finance services and solutions keep your business forefront of the market. To know more about what kind of use cases of AI for finance we provide to clients.

Here are the few significant applications of AI for finance.

  1. CREDIT DECISIONS

AI in financial services is a game-changer now. In particular, it detects irregular patterns and prevents unknown to attempt illegal acts.

The application of credit decision also comes under this genre. AI in finance helps credit lenders make smarter decisions while disbursing loans to borrowers. Credit lenders using AI can easily get to know the borrower’s repayment ability by surfing their credit score securely.

Here are few AI companies which are supporting the financial industry by delivering next-level AI solutions for finance.

#1 ZESTFINANCE

Location: Los Angeles, United States

 How it is utilized AI for credit lending?

ZestFinance’s Zest Automated Machine Learning (ZAML) AI software makes mortgage lending more safe and secure. ZAML is an AI-enabled finance solution that helps lenders to evaluate borrowers’ credit scores and financial data. So, this AI-powered tool for finance prevents credit risks and generates the best loan recovery rates for financers.

#2 DATAROBOT

Location: Boston, United States

What this AI company has developed for finance?

DataRobot’s AI solution for finance helps financing companies know credit card frauds, illegal transactions, and many more. Using the power of ML, AI solutions can deliver precise predictive models. Such models help financers make decisions into fraudulent acts.

USM has a proven track record of delivering similar AI-driven apps for finance and banking service providers. Let’s talk to our AI expert about getting your app into reality.

Recommend: How the blend of Blockchain & AI change the face of businesses?

 

#3 SCIENAPTIC SYSTEMS

Location: NYC, United States

How it is utilizing Artificial Intelligence in finance?

Scienaptic Systems’ AI platform provides clear information to credit organizations and banks. The company has over 100 million customers till yet. It can connect both structured and unorganized data, process it, and provides intelligence to service providers.

#4 UNDERWRITE.AI

Location: Boston, United States

How it is utilizing Artificial Intelligence in finance?

Underwrite.ai studies the ample number of data points from credit agency sources to evaluate credit risk. Likewise, it helps service providers to know the repayment capability of borrowers.

Using ML models, the software extracts patterns and decides the loan payment capability of applicants. Such an intelligent process reduces human errors and accurately identifies the proper candidates to disburse credit.

Recommend: AI in Accounting and Finance: How AI Will Impact The Accounting & Finance Industry?

 

  1. MANAGING RISK

The finance industry is becoming more inclined towards ML methods. ML technology helps financial advisers to utilize more accurate data and identify patterns in their customer’s search. Hence, the possible risks will be predicted by the AI software before they damage credit data.

The following Artificial intelligence companies are helping the financial and banking firms to control the risk.

#1 KENSHO

Location: Cambridge, Massachusetts, United States

How it’s utilizing Artificial Intelligence in finance?

Kensho offers AI and data analytics solutions to financial firms. The software provides analytical solutions by integrating NLP and cloud computing technologies. The intelligent systems that use Kensho’s software deliver accurate predictions to the upcoming risks in a simple English language.

We do have similar AI-enabled mobility solutions for banking and financial companies. Our multi-purpose insurance applications deliver more personalized services to banking customers.

#2 AYASDI

Location: Menlo Park, California, United States

How Ayasdi utilizing AI technology for finance?

To deal with financial challenges, Ayasdi has developed cloud-based and intelligent smart solutions. This AI Company helps realize and handle risks and predict the actual customer needs in this evolving fintech space.

Recommend: Artificial Intelligence Solutions For Banking and Finance Industry

 

  1. QUANTITATIVE TRADING

Quantitative trading is a process of organizing vast data to extract regular patterns for making qualitative trades.  AI systems analyze complex data in a matter of minutes ad derive patterns. So, the adoption of AI in financial services automates trading and saves a lot of time for the workforce.

The below listed are few enterprises that use AI technology to deliver smart applications for finance.

#1 ALPHASENSE

Location: NYC, United States

How it uses Artificial Intelligence in finance?

Alphasense is an AI-driven search engine used by banking and financial firms. It uses NLP to study keyword searches and provide insights and valuable data to users with high speed.

#2 KAVOUT

Location: Bellevue, Washington, United States.

How it’s utilizing Artificial Intelligence in finance?

Artificial intelligence in banking and finance is a focal stream of Kavout. The company uses quantitative analysis and ML models to handle large data and analyze them to provide insights.

Kavout’s AI-enabled stock ranker examines massive data sets like cost patterns and SEC filings. Later, it summarizes the data into rank for stocks. The more the KAI Score, the excellent the stocks will perform in the financial market.

USM is one of the biggest artificial intelligence companies. We have decades of experience in using AI for developing the next-level solutions for finance and banking companies.  Our AI solutions for finance ensure high-security while automating the entire process. Check out our AI  investment mobile app now

#3 ALPACA

Location: San Mateo, California, United States

How it’s utilizing Artificial Intelligence in finance?

Artificial intelligence in banking using deep learning gives tremendous results. Alpaca integrates deep learning technology and data storage to offer predictive AI applications for finance. It finds price variations of a product in the market and forecasts the growth in the future.

  1. PERSONALIZED BANKING

#Artificial Intelligence in banking

In this digital era, everything needs to be faster and smoother. You need not wait in a queue for taking a bank statement or cash transfers. Intelligent technology has brought everything to your fingertips.

AI in banking is a buzzword and enlighten the ways of traditional banking services. It is estimated that over 75% of banking customers are using tools for managing their accounts. Moreover, unlike direct meeting financial advisers, 41% of clients are opting for digital advisers.

AI chatbots are the best examples of personal assistants. While interacting with clients like bankers, they advise customers. Thus, Artificial Intelligent virtual assistants and chatbots in banking provide personalized financial services.

USM is delivering corporate banking mobility solutions. Our user-friendly banking mobile apps successfully launched on global banks and witnessed the fruitful performance.

USM’s AI-driven banking apps!

The below are a few AI companies that help finance and banking customers offer personalized banking services to their customers.

#1 KASISTO

Location: NYC, United States

How the company is utilizing AI in finance?

Kasisto helps banks and financial institutions to reduce call center tasks. Its AI platform helps and responds to the client’s query 24*7. Thus, this kind of solution improves customer experiences and achieves user satisfaction.

#2 ABE AI

Location: Orlando, Florida, United States.

 How it uses AI technology in finance?

Abe AI developed an AI-driven banking virtual assistant. It enables banks and financial firms to provide an enhanced version of customer care service. This banking assistant supports Facebook, SMS, Amazon Alexa, and Google Home, mobile to offer customers more comfortable and convenient banking services.

#3 TRIM

Location: San Francisco, United States

How is using AI technology in finance?

Trim is an AI-enabled virtual assistant. It connects user-profiles and gives them a brief on their spending.

This innovative finance app helps account holders save money and put their investments in FDS for enjoying ROI. This AI-powered personal assistant advises you to stop all your money-wasting subscriptions and lets you save your valuable money.

 Want to develop such an amazing smart financial app! We help you succeed in launching your project ideas alive!

 USM’s AI-based investment mobile app gives customers insights into their investments. Let’s talk deep about this Artificial intelligence banking project.

 

  1. AI for FRAUD DETECTION

A large number of digital transactions are taking place every day. Customers pay their bills, make transactions, and invests in stocks digitally. So, here AI prevents fraudulent acts in the banking and finance industry.

Here are leading AI companies that offer AI-powered security solutions for banking and financial firms.

#1 SHAPE SECURITY

Location: Mountain View, California, United States

How is it using the artificial intelligence platform in finance?

Shape Security software detects breaches instantly. Its AI-powered bots by asking security questions allow users to open their accounts securely with proper credentials. It can identify fraud entries, credential stuffing for providing added safety to customers’ accounts.

#2 DARKTRACE

Location: Cambridge, Massachusetts, United States

How DarkTrace company utilized AI in finance?

For many industries and financial institutions, Darktrace builds cybersecurity solutions. Using ML models, its solutions analyze the network data and find out unexpected risks before the entire network systems got damaged.

Conclusion

Finally, AI in finance and AI in banking help banks protect accounts from hacks. This advanced AI technology in banking prevents credential stuffing by hackers. If you want to know more about AI-powered finance solutions, contact us now.

“Invest in artificial intelligence today and make your banking or finance more interactive with customers in a secure way.”

Source: https://usmsystems.com/top-ai-companies-in-finance/

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Aite survey: Financial institutions will invest more to automate loan process

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Financial institutions plan to increase their spend on automations and collections management solutions for their loan processes. Fresh results on consumer lending practice from research and advisory firm Aite Group indicate lenders plan to invest more heavily in their collections processes, said Leslie Parrish, senior analyst for the Aite Group’s consumer lending practice. Parrish shared […]

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Source: https://bankautomationnews.com/allposts/lending/aite-survey-financial-institutions-will-invest-more-to-automate-loan-process/

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Facial recognition, other ‘risky’ AI set for constraints in EU

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Facial recognition and other high-risk artificial intelligence applications will face strict constraints under new rules unveiled by the European Union that threaten hefty fines for companies that don’t comply.

The European Commission, the bloc’s executive body, proposed measures on Wednesday that would ban certain AI applications in the EU, including those that exploit vulnerable groups, deploy subliminal techniques or score people’s social behavior.

The use of facial recognition and other real-time remote biometric identification systems by law enforcement would also be prohibited, unless used to prevent a terror attack, find missing children or tackle other public security emergencies.

Facial recognition is a particularly controversial form of AI. Civil liberties groups warn of the dangers of discrimination or mistaken identities when law enforcement uses the technology, which sometimes misidentifies women and people with darker skin tones. Digital rights group EDRI has warned against loopholes for public security exceptions use of the technology.

Other high-risk applications that could endanger people’s safety or legal status—such as self-driving cars, employment or asylum decisions — would have to undergo checks of their systems before deployment and face other strict obligations.

The measures are the latest attempt by the bloc to leverage the power of its vast, developed market to set global standards that companies around the world are forced to follow, much like with its General Data Protection Regulation.

The U.S. and China are home to the biggest commercial AI companies — Google and Microsoft Corp., Beijing-based Baidu, and Shenzhen-based Tencent — but if they want to sell to Europe’s consumers or businesses, they may be forced to overhaul operations.

Key Points:

  • Fines of 6% of revenue are foreseen for companies that don’t comply with bans or data requirements
  • Smaller fines are foreseen for companies that don’t comply with other requirements spelled out in the new rules
  • Legislation applies both to developers and users of high-risk AI systems
  • Providers of risky AI must subject it to a conformity assessment before deployment
  • Other obligations for high-risk AI includes use of high quality datasets, ensuring traceability of results, and human oversight to minimize risk
  • The criteria for ‘high-risk’ applications includes intended purpose, the number of potentially affected people, and the irreversibility of harm
  • AI applications with minimal risk such as AI-enabled video games or spam filters are not subject to the new rules
  • National market surveillance authorities will enforce the new rules
  • EU to establish European board of regulators to ensure harmonized enforcement of regulation across Europe
  • Rules would still need approval by the European Parliament and the bloc’s member states before becoming law, a process that can take years

—Natalia Drozdiak (Bloomberg Mercury)

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Source: https://bankautomationnews.com/allposts/comp-reg/facial-recognition-other-risky-ai-set-for-constraints-in-eu/

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Prioritizing Artificial Intelligence and Machine Learning in a Pandemic

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AI and ML
Illustration: © IoT For All

Artificial Intelligence (AI) and Machine Learning (ML) give companies the one thing humans can’t – scalability. Over time, humans limit a businesses’ ability to scale; they can only work so many hours at a given efficiency. On the other hand, AI and ML can work around the clock with the sole focus on a given project. As organizations navigate through COVID-19’s impact and the future of a remote workforce, scalability and efficiency can be the key to an organization’s successful recovery.

Implementation Challenges

The benefits of AI and ML don’t come without their own challenges; however, the top challenges are a lack of skills and time for proper implementation. In July, Deloitte found in a survey that 69% of respondents said the skills gap for AI implementation ranged from moderate to major to extreme. Simultaneously, many companies overlook the investment it takes to build the processes and infrastructure needed for successfully training, testing, deploying, and maintaining AI and ML in their enterprise.

Such challenges often cause companies to de-prioritize AI and ML projects, especially in times of uncertainty. That has been increasingly obvious throughout the COVID-19 pandemic. But while some organizations have drawn back on their efforts, the current global state demands the greater need for AI and ML to support critical business processes. This is especially true today given the growing remote workforce, considerations for returning to the workplace and work happening in silos worldwide.

Though challenging, it is not impossible to properly implement AI and ML. In this evolving COVID-influenced business landscape, four steps are key to effectively implementing a strong AI and ML system that helps streamline critical business processes despite uncertainty and limited resources.

Identify the Problem to Be Solved

Some companies mistakenly view AI and ML projects as a ‘silver bullet’ to solve all their problems. This often results in overinflated expectations, an unfocused approach, and unsatisfactory results. Instead, companies should identify those specific problems that will have the biggest impact from implementing AI and ML solutions and be hyper-focused on solving those problems.

Select Your Data

The second step in creating a strong AI and ML algorithm is to select the source data that your algorithm will be training on. There are two main options: training on your own data or training on a larger scale data set. Based on experience, training your algorithm on your own data puts you at a disadvantage. By training on a larger scale data set, the likelihood of success increases because your data is more representative and varied. Through advanced concepts such as transfer learning, companies can use semi-trained models based on larger data sets and then train the “last mile” using their own specific content unique to their business.

Clean House

The standby rules of data management apply here – garbage in, garbage out. Ultimately, the quality and accuracy of machine learning models depend on being representative. AI and ML – fed with the right data – can streamline operations and increase the benefit of companies’ DX and cloud migration journeys.

When you’re kicking off an AI or ML project, the most critical step is to clean up the data that your algorithm will be training on, especially if you’re using your own data or models.

Make Room for Training

AI and ML are all about probability. When you ask it a question, for example, “Is this a cat?,” the results you receive are the algorithm saying, “Out of the three buckets I was trained on, the likelihood of this image being a cat is .91, the likelihood of this image being a dog is .72 and the likelihood of this image being a bird is .32.”

This is why training on varied data is so important. If your training data only includes images of cats, dogs, and birds and you ask the algorithm to analyze the picture of a crocodile, it will only respond based on the buckets it’s been trained on – cats, dogs, and birds.

If you’ve properly selected and cleaned your data, training should be an easy last step, but it’s also an opportunity to go back to the first two steps and further refine based on your training.

The front end of training an AI and ML algorithm can be time-intensive, but following these four steps can make it easier to achieve significant outcomes. Across industries, AI and ML can quickly show ROI. For example, in the insurance industry, AI and ML can help insurers quickly search contracts, so employees aren’t sifting through contracts and repositories around the globe to answer simple questions. This means time efficiencies for an industry that COVID-19 has heavily impacted.

Even better, working with a SaaS provider with experience in your industry can make this process much easier and less costly. SaaS platforms allow companies to take advantage of having all of the infrastructure, security, and pre-trained models in place to reduce the overall effort and time to value. Many platforms allow users to uptrain the predefined models with unique customer data, reducing the training effort needed for model creation. Companies can then focus on integration with their ecosystem and workflows rather than model creation itself.

Bigger Picture

Overall, businesses can soften the impact of COVID by focusing on the bigger picture with AI and ML. Implementing AI and ML projects increase business productivity despite these times of uncertainty. As we continue on the road to recovery, we need tools like AI and ML to stay focused on the bigger picture, mission-critical tasks.

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Source: https://www.iotforall.com/prioritizing-artificial-intelligence-and-machine-learning-in-a-pandemic

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ProGlove promotes worker well-being with human digital twin technology

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ProGlove, the company behind an ergonomic barcode scanner, has developed new tools for analyzing human processes to build a human digital twin.

“We have always been driven to have our devices narrate the story of what is really happening on the shop floor, so we added process analytics capabilities that allow for time-motion studies, visualization of the shop floor, and more,” ProGlove CEO Andreas Koenig told VentureBeat.

The company’s newest process analytics tools can complement the typical top-down perspective of applications by adding a process-as-seen view to the conventional process-as-wanted view. Most importantly, it can also provide insights that improve well-being.

Koenig said, “We are building an ecosystem that empowers the human worker to make their businesses stronger.”

ProGlove CEO Andreas Koenig

Above: ProGlove CEO Andreas Koenig

Image Credit: ProGlove

The market for barcode scanning is still going strong and is often taken for granted, given how old it is. “You have technologies like RFID that have been celebrated for being the next big thing, and yet their impact thus far hasn’t been anywhere near where most pundits expected it,” Koenig said.

Companies like Zebra, Honeywell, and Datalogic have lasted for decades by building out an ecosystem of tools to address industry needs. “What sets us apart is that we looked beyond the obvious and started with the human worker in mind,” Koenig said.

Not only is the company providing a form factor designed to meet requirements for rugged tools, this shift to analytics could further promote efficiency, quality, and ergonomics on the shop floor.

How a human digital twin works

ProGlove’s cofounders participated in Intel’s Make It Wearable Challenge, with the idea of designing a smart glove for industries. Today, ProGlove’s MARK scanner can collect six-axis motion data, including pitch, yaw, roll, and acceleration, along with timestamps, a step count, and camera data (such as barcode reading speed and the scanner ID).

Koenig’s vision goes beyond selling a product to establish the right balance between businesses’ need for profits and their obligation to ensure worker well-being. Koenig estimates that human hands deliver 70% of added value in factories and on warehouse floors. “There is no doubt that they are your most valuable resource that needs protection. Even more so since we are way more likely to experience a shortage of human workers in the warehouses across the world than having them replaced by robots, automation, or AI.”

ProGlove Insight contextualizes the collected data and lets users compare workstations and measure the workload and effort necessary to complete the tasks. Users can also visualize their shop floor, look at heatmaps, and identify best practices or efficiency blockers. After a recent smart factory lab experiment with users, DPD and Asics realized efficiency gains by as much as 20%, Koenig said.

ProGlove’s vision of the human digital twin is built on three pillars: a digital representation of onsite workers, a visualization of the shop floor, and an industrial process engineer. “The human digital twin is all about striking the right balance between businesses’ needs for profitability, efficiency, and worker well-being,” Koenig said. At the same time, it is important that the human digital twin complies with data privacy regulations and provides transparency to frontline workers around what data is being transmitted.

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Source: https://venturebeat.com/2021/04/21/proglove-promotes-worker-well-being-with-human-digital-twin-technology/

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