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Machine learning: the need for hours for banks Artificial intelligence

Over the past few decades, banks have been leaders in introducing new technological innovations and have taken advantage of all that technology has to offer.

British inventor of Indian descent John Adrian Shepard-Baron was inspired by chocolate vending machines and came up with the idea “if vending machines can hand out chocolates, why can’t they give out cash?” and presented the idea to the head of Barclays Bank. They fell in love with the concept, and the first ATM was installed near Barclays Bank in North London in June 1967.

With the invention of the ATM, customers were no longer limited by the hours and location of banks and could easily access their money.

Similar technological milestones were reached when banks introduced electronic and card-based payments in the 1970s, Internet banking and the widespread use of mobile banking emerged in the 1990s and early 2000s, gaining popularity in early 2010. -x.

Very few will disagree that we are now in a digital age that is supported by lower storage and maintenance costs, increased connectivity and access for all, and rapid advances in niche technologies such as artificial intelligence (AI) / machine learning (ML).

ML is a branch of AI that uses algorithms to study data with the least amount of human intervention. ML can lead to higher process automation and, when deployed after risk control, can improve bank transaction processing and decision-making in terms of speed, accuracy and compliance.

The global AI and ML market in the BFSI market was estimated at $ 7.66 billion in 2020 and is projected to reach $ 61.24 billion by 2030, an average increase of 23.1% from 2021 to 2030 , according to an Allied Market Research report.

This forecast highlights the huge potential in the AI ​​/ ML space, where both financial institutions and customers can gain a more complete and rewarding experience.

Boston Consulting Group has estimated that banks implementing ambitious AI and ML strategies could add 15-20% to profits in one to two years. In addition, McKinsey expects that ML technology in banking can potentially reachup to $ 1 trillionannually added value to the global banking industry.

The pandemic has become an unprecedented catalyst for the digitalization of banks around the world. ML is constantly evolving, and FinTech will continue to be one of the leading industries enjoying AI / ML capabilities.

Here are some areas where banks can benefit greatly from using ML:

Customer selection:

Connecting a customer requires a variety of rules and includes lengthy negotiations, rigorous documentation, and sophisticated products and services.

Traditional adaptation, especially for corporate clients, takes 2-3 weeks, as the client is required to prepare various documents and time-consuming processes.

The report clearly states that 35% of the bank’s customers switch to competitors due to poor adaptation experience.

According to the study, the key guideline is that the adaptation time should be less than 3 minutes, which reduces the dropout percentage by 20-30%.

The adaptation experience can really determine a client’s current relationship with the organization. Customers expect from the bank the same benefits as from other companies such as Google, Amazon and Facebook.

This puts pressure on banks to replicate the same experience, while there is no compromise in compliance checking and KYC.

The award-winning Muinmos customer adaptation platform with AI / ML claims to be able to connect any retail, professional or institutional customer worldwide in 3 minutes.

“Banks can complete the registration of retail customers on our platform in less than three minutes without having to complete the documentation manually or visit branches for KYC,” – says Bankbuddy.AI

Customer content:

Customer retention metrics are factors or variables used to measure the likelihood of customer retention. This vital metric helps identify and persuade customers before they decide to switch to other products or services. Its cost is much higher to add a new customer than to retain an existing one.

Any multibillion-dollar bank typically reviews thousands of requests every minute, whether it’s a request related to the status of credit sanctions, credit improvements, documentation, or for a request about the status of an existing request.

Most requests are similar or recurring, and some require real-time customer support. Banks can use ML to automate and consolidate their backend and support processes.

Chatbots with ML provide intelligent personalized real-time assistance and an enhanced experience, saving a lot of human and organizational effort for the organization.

Chatbots learn from each request, and conversations become more personalized over time. They play a crucial role in customer retention as well as in customer satisfaction.

One notable example of chatbot success is ERICA from Bank of America, an ML-based assistant in their mobile app.

According to the BOA’s annual report for the 4th quarter of 2021, ERICA has 24.6 million active users. At the start of the pandemic, Eric added 1 million users per month from March to May 2020, bringing the number of users to 14 million by the end of May 2020.

ERICA also fulfilled more than 400 million customer inquiries in 2021, recording a staggering 418% increase in customer interactions compared to last year.

Compliance with regulatory requirements:

Regulations Complying with federal and international regulations helps protect banks from a variety of risks. The conservation process is costly due to the rapidly changing regulatory landscape.

According to a recent Citigroup report, global financial institutions spend about $ 270 billion annually on risk-related and regulatory compliance, accounting for nearly 10% of their total operating costs. It is estimated that almost half of compliance costs are spent on technology.

To address these challenges, banks use ML to make regulatory compliance easy to manage, scalable, and less expensive for their business. ML can help dramatically reduce, if not eliminate, false positives in compliance systems.

In most banks, these alerts are reviewed by supervisors. ML algorithms can be learned from the data of the control officer.

They can increase efficiency and accuracy by spending only a fraction of the time and thus reduce costs by raising an alarm only if the detection system is uncertain and requires human experience. Compliance.ai, the advantage in terms of requirements, Jumio – are several well-known providers of solutions in accordance with the requirements.

Fraud detection:

Fraud is “the crime of deceiving someone in order to obtain money or goods illegally,” according to Oxford’s definition. According to the study, losses due to financial fraud amounted to $ 32 billion in 2020 and are estimated to exceed $ 40 billion by 2027.

Fraud can occur in a variety of common forms such as phishing, identity theft, money laundering, mobile fraud, ghostly invoices, stolen cards, etc.

The most popular way to mitigate losses in financial institutions is to use a fraud detection algorithm, and, according to a study, about 60% of banks use the benefits of ML in the fight against payment fraud.

ML systems learn from previous experiences and get better over time. They drastically require less human intervention, detect fraud on their own and take appropriate measures to stop it.

The Forrester Wave ™ Enterprise Fraud Management report, Q3 2021, rated the top eight corporate fraud vendors on 35 criteria and rated NICE Actimize’s Integrated Fraud Management product and SAS’s SAS Enterprise Fraud Solution as leaders in this category.

Credit solutions:

It usually takes a day to a week to get a loan approved in the traditional banking process. The first banks with AI / ML developed and streamlined the lending process, using extensive automation and real-time customer data analysis to generate operational lending solutions for retail and corporate customers.

“Credit applications and underwriting are key areas where machine learning and data analytics in general will have an initial impact. The results will include lower costs, increased efficiency and less demanding customer experience, ”experts say.

Peer to Peer lending has aroused great interest from both lenders and borrowers. Along with P2P lenders, traditional banks also use ML solutions to increase market share without additional risks.

ML processes for making credit decisions on FICO scores and income and social profile data, telecommunications usage data, location data, utilities, rent payments and even health review records and then generate an accurate risk metric.

Assume that the risk indicator is within the threshold set by the lender, the loan is approved automatically. All this evaluation takes a couple of minutes.

ACTICO is a leading international provider of software for intelligent automation and digital solutions.

Conclusion:

We have entered the era of AI and ML, and the use of ML in banking is constantly growing. ML is transforming the banking and financial services industry to streamline and optimize processes ranging from credit solutions to quantitative trading and financial risk management. In addition, it helps improve customer experience, services and reduce fraud by tracking transactions to detect suspicious transactions with compliance issues.

Technologies like ML and AI are no longer the future – they are the present – it’s time to catch up with the trend and they are the need of the hour.

Cigniti, with his experience of being the core banking digital transformation and testing partner of advanced mobile banking in the US and UK, has proven its capabilities in areas such as multi-channel banking, retail banking, corporate banking, centralized banking, mortgages, cards and payment gateways. Cigniti has extensive experience in testing a variety of industry standard products such as T24, Finacle, Flexcube, Bancs24 and Vision Plus, and complies with regulations such as BASEL, BCBS 239, SEPA, AML, FATCA, etc.

Schedule a discussion with our experts in banking and digital transformation to learn more about why ML is a time necessity for the banking industry.

  • Vishnu has almost 18 years of experience in advising, implementing and testing leading major banking products. Vishnu is an associate chief practice consultant for BFSI and Cigniti’s Centers of Excellence, which focuses on building deep expertise in the field and developing solutions to the challenges facing the industry.

https://www.cigniti.com/blog/machine-learning-banks/ Machine learning: the need for hours for banks Artificial intelligence

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