Improving decision-making for loans and credit
Similarly, banks are using AI-based systems to help make more informed, safer, and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit scores, credit history, customer references, and banking transactions to determine whether or not an individual or company is creditworthy.
However, as many will attest, these credit-reporting systems are far from perfect and are often riddled with errors, missing real-world transaction history, and misclassifying creditors. In addition to using data that are available, AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with limited credit history might in fact make a good credit customer or find customers whose patterns might increase the likelihood of default.
The challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues similar to their human counterparts. This is due to how loan decision-making AI models are trained. Banks looking to use machine learning as part of the real-world, in-production systems need to make sure to factor bias and ethics into their AI training processes to avoid these potential problems. This is especially the case when using AI algorithms, such as deep learning approaches, that are inherently unexplainable.
The issue of explainability is another potential stumbling block. Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers. This makes it difficult to implement tools built around neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human mind. Explaining the decisions of neural networks is challenging and can negatively affect the customer experience.
Reducing bank operating costs and risk
The bank industry is largely digital in operation, but it is still riddled with human-based processes that sometimes are paperwork-heavy. In these processes, banks face significant operational cost and risk issues due to the potential for human error. AI in banking is being applied to these processes to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms, and other sources. Improved handwriting recognition, natural language processing, and other technologies, combined with intelligent process automation tools, are being used more and more in back-office operations to handle a wide range of banking workflows.
In addition, by replacing these human processes with AI-based automation, banks can impose audit and regulatory control where it previously has been unable to do so. By replacing humans with intelligent, automated assistants, banks can focus their human resources on higher-value tasks, such as offering new services to their customers or improving customer satisfaction. According to Accenture, banks are seeing between 20% to 25% savings in their operations through the implementation of intelligent assistants and AI-based systems in their back-office workflows.
AI assistants for investing
Finally, some banks are delving deeper into the world of AI by using their smart systems to help make investment decisions and support their investment banking research. Firms like Switzerland-based UBS and Netherlands-based ING are having AI systems scour the markets for untapped investment opportunities and inform their algorithmic trading systems. While humans are still in the loop with all these investment decisions, the AI systems are uncovering additional opportunities through better modeling and discovery.
In addition, many financial services companies are offering Robo advisers that can help their customers better manage their money. Through personalization, chatbots, and customer-specific models, these Robo advisers can provide high-quality guidance on investment decisions and be available whenever the customer needs their assistance.
In all these ways, AI in banking is continuing to transform the industry to provide greater levels of value to their customers, reduce risks and increase opportunities involved in being the financial engines of our modern economy.
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WRITTEN BY
venkat vajradhar
We USM Business provide unique edge solutions related to AI services, Ml Services, Data Quality Solutions, & Permanent staffing solutions.https://www.usmsystems
From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century.
It has also enabled users to make faster and more informed decisions with an increased amount of efficiency.Of late, the banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways.
The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.One of the first steps was taken in 2015 by Ally Bank (USA) — introducing Ally Assist — a chatbot that could respond to voice and text, make payments on behalf of the customer, give an account summary, monitor savings, spending patterns, and use natural language processing to understand and address customer queries.Banks all over the world followed up with their best versions of chatbots: Erica to iPAL, Eva and the most famous one — SBI’s SIA.
According to Payjo (the start-up which developed SIA), SIA can handle up to ten thousand inquiries per second and is one of the world’s largest deployments of artificial intelligence in consumer-facing banking.
Banks are increasingly spending on artificial intelligence and ML in data analytics for personalized and faster customer experiences to garner the interests of the tech-savvy and the millennial class.To Know About: Rise of AI-Powered Chatbots in the Banking IndustryAccording to the FinTech Trends India Report by PwC in 2017, the global spending in artificial intelligence has touched $5.1 billion.
It includes applications and payment interfaces, digital wallets, chatbots, or interactive voice response systems.
OpenAI is looking forward to this feat for both robotic add-ons and its own AI software, which allows Docktail to learn new things using virtual simulations, before overcoming a real, physical challenge.In a performance video featuring Docktail’s new talent, the robotic arm with clumsy yet precise maneuvers leads to a complete cube solution.
“Velander has been referring to a range of robots for the past few years that have pushed the Rubik’s Cube solution beyond the confines of human hands and minds.
In 2016, semiconductor manufacturer Infineon specifically developed a robot to solve the Rubik’s Cube at superhuman speed, and the bot was able to do so in under a second.
As Wallander notes, OpenAI’s ongoing robotics work is not only about achieving superior results in narrow tasks but also because you need to develop and program a good robot.
It has been trained using software that is currently trying to be fundamental to reflect the millions of years of evolution that help children learn to use our hands naturally.
One day, OpenI’s hopes will help mankind develop the humanoid robots we know only from science fiction, robots that can safely operate in society without risk, and perform a variety of tasks in a chaotic environment such as city streets and factory floors.To learn how to solve a Rubik’s Cube with one hand, OpenIII is not explicitly programmed by Docktail to fix the toy; Free software on the Internet can do it for you.
With this Big Data Parts of Digital Future technology becoming more ubiquitous, shoppers can make informed decisions using online data and content to discover, compare and buy products from anywhere, anytime.Uses of AI on Retail Industry in 2021: Image source: hacker noonChatbots to help with customer service:AI chatbots provide an even higher level of customer service, improve searches, send notifications about new collections, and suggest similar products.
Tommy Hilfiger and Burberry have launched chatbots to help their customers navigate their collections.Price adjustments:AI applications for retail stores could help companies set prices for their products, visualizing the likely results of multiple pricing strategies.
Mood tracking will definitely help build stronger customer relationships.Big Data Trends Revolutionizing Retail and Improving Retail Analytics: Image source: ZDNetRetailers can embrace a new geo-targeting phenomenon to better target potential customers within the store.
By utilizing the in-store sales funnel, marketers can come up with the best opportunities that deliver the highest ROI from their marketing efforts.Big Data allows retailers to feed their ideas, including cut, color and price, to an algorithm that uses sentiment analysis to predict whether or not the ideas will be a hit with consumers.It also helps retailers react more quickly to competition and electronic competition, market changes, and other macro-environmental factors by recognizing early signs of growing demand or slowing down in terms of sales.
It helps to assign sku to the stores where the demand would be and price it in a way, so it would bring the maximum gross margin.AI is transforming the retail industry in 2021: Image source: itechtionsThe AI Transforming your business in the Retail Industry has seen a massive change in its base of operations in recent times, with many large brands choosing to reduce the number of on-site offerings in favor of online service.
The reason for this is simple, customers are now looking for the path of least resistance when buying their clothes, and this is best reported on the Internet.
An effective end-to-end CCM solution can also help banks reduce communication costs.Increasing client communications can often mean sending more communications and making it harder to estimate the cost.
What if clients agree to have digital communication, but are not fully paperless?
These ‘double dippers’ want digital communication facilities but are not yet ready to give up the habit of receiving envelopes in the mail.
This doubling of costs, while positive for your omnichannel communication strategy, can be devastating to your budget.In two years, the percentage of digital users alone is expected to increase to 36%, down from 31%, at a print-only price.
This means that banks must either adjust their communications budgets to accommodate double dumpers or find innovative ways to encourage them to go completely paperless.Seek cost confirmation while preserving client engagement.Unexpected communication costs can wreak havoc with the bank’s annual budget.
ROI calculations become more accurate when the annual fee (ex-post) is fixed.All you need is cost assurance — an expected fee structure that covers all communications in print and digital.
