How the most powerful institutions in the world are using ......adoption. With digital...

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JUNE 2018

How the most powerful financial institutions in the world are using artificial intelligence

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Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry – think chatbots, PFM, robo-advisors, and so on.

Banks are never the ones to be left behind when it comes to tech adoption. With digital transformation and customer experience as the top most priority, banks are now banking on AI to deliver the next-gen service to their customers. Some of the most powerful banking and financial institutions are looking to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI.

There is a variety of use cases and application-areas for AI in financial services. Whether you think of a conversational interface, software robots, recommendation engines, automated AML checks, behavioral analytics & profiling, real-time fraud detection, insightful trading, etc. –

AI has a huge array of use cases. However, all these use cases can be categorized into four major categories: Front-Office (customer-Focused), Back-Office (operation-focused), Regulatory Compliance, and Trading/Portfolio Management.

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As part of our recent study, we analyzed 34 major banks across several geographies (i.e., US, EU, Singapore, Africa, Australia, India) to understand the trends and developments in their AI initiatives – which include projects, pilots, and experiments.

As an outcome, we found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space are Bank of America, OCBC, ABN Amro, YES BANK, etc.

Out of these 34 banks, 25 banks are using AI for their operation-specific back office processes with the use cases ranging from process automation, fraud detection, real-time authentication, intelligent receivables etc. E.g. Bank of America, ANZ Bank, ICICI Bank, NatWest, Lloyds Bank, etc. Bank of America partnered with HighRadius and launched Intelligent Receivable, a new service that uses AI to improve straight-through reconciliation of incoming payments

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Lloyds Banking group has partnered with Pindrop, a US-based AI startup, and has implemented its AI/ML-based solution to detect fraudulent phone calls.

Out of these 34 banks, 8 are implementing AI-based cognitive capabilities in their trading and portfolio functions, with the use cases ranging from real-time trading insights, investment research to trade-matching tool, etc.

Some of the leading banks in this category are BNP Paribas, Credit Suisse, Goldman Sachs, Barclays, etc.

Out of these 34 banks, 8 are leveraging AI-based solutions in their regulatory compliance functions, with the areas of applications ranging from automated data management, reporting, AML, compliance, automated regulation interpretation & mapping, etc.

Some of the leading banks in this category are OCBC Bank, Commonwealth Bank, Wells Fargo, HSBC, CITI, etc.From the study, we could understand that the biggest focus of banks with their AI initiatives is conversational interfaces and virtual assistants, closely followed by process excellence and fraud detection. While a relatively fewer number of banks are looking at AI in compliance and trading functions, there is no shortage of opportunities to be explored in these spaces. As the year 2018 unfolds, we may expect several new acquisitions/partnerships by banks and AI startups. These partnerships will play a major role in defining the perimeter for the evolving AI strategies of banks.All in all, AI in banking and financial services is a volcano waiting to erupt.

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AUTOMATION

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RPA is expected to have a $6.7 trillion global economic impact, a plan that will result in a 40-45% growth of global spending on technology1. In addition, the estimated global market potential of RPA stands at $8.75 billion by 2024. Automation is one of the more explored areas of AI/ML application.In the United States, Barclays Bank has implemented RPA across various processes which include but are not limited to – fraud detection, risk monitoring, account receivables processing, and loan application. With the implementation of RPA enhancing productivity, Barclays was able to save over £175 million annually in bad debt provisioning.Another large financial institution has focused on back-office automation through RPA. The bank reported that the implementation of RPA has led to the following results: 100% accuracy in account-closure validations across five systems, 88% improvement in processing time, 66% improvement in trade entry turnaround time, ¼-second robotic reconciliation of a failed trade vs. 5-10 minutes by a human. The activity of its fund transfer bots alone is responsible for $300,000 in annual savingsOne of the latest and probably most vivid indicators of transformative processes in banking is Goldman Sachs, which brings significant automation into areas of trading like currencies and futures using complex trading algorithms, some with machine learning capabilities.Citibank invested in Ayasdi and is using its technology in several use cases including automating data-to value processes, reducing time to insights.1https://gomedici.com/robotic-process-automation-market-will-have-8-75-billion-global-market-potential-by-2024-infographic/

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Europe also has its examples – a securities services function for a major institution in Europe is employing a trade-matching tool using artificial intelligence and predictive analysis to further automate the trade processing services it provides to investment managers.

Using predictive analysis, the solution analyzes historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. The bank is already making good progress, having reached around 98% prediction accuracy.

Financial institutions in the APAC region have also been adopting for AI for automation purposes. ICICI Bank has deployed software robotics functions across the organization including retail/wholesale/agri banking operations, treasury operations, human resources management, international remittances, and private banking. The software robots are configured to capture and interpret information from systems, recognize patterns, and run business processes across multiple applications to execute activities including demographic data validation & updation, data formatting & consistency verification, multi-format message creation, text mining, workflow acceleration, and reconciliations. A few of the AI technologies that the platform uses are natural language processing, ML, cognitive tools, optical character recognition, and data analytics.

New Zealand’s largest financial services group implemented RPA across its four global delivery hubs. Rapid implementation in smaller units by decentralizing the adoption process enabled them to achieve quick ROI while reducing costs by 40% and reducing end-to-end delivery time.

AUTOMATION


A large Singaporean institution is working with IBM to scale an enterprise-wide Centre of Excellence (COE) in Robotic Process Automation (RPA), making it the first-of-its-kind large-scale implementation in the financial services sector in Singapore and the region. IBM helped the bank set up a COE in RPA in June 2017; by December 2017, the bank had applied RPA to 50 complex business processes across its operations, freeing up 25,000 man hours. Now the RPA program will be progressively implemented in other markets, including Hong Kong, China, India, Indonesia, and Taiwan.

Axis Bank, India’s third-largest private sector bank, has implemented AI across 125+ processes and cognitive automation across 90 processes to reduce manual labor and turnaround time.

Another large private sector bank in India has a particularly interesting application: the bank has partnered with Anytime Loans, an automated P2P lending platform, which uses AI to read facial features and feed it into a predictive model that determines the borrower’s propensity to default. The company has disbursed $10.18 million over the last 30 months to 38,700 entities for personal loans, business loans, and education loans. Its gross defaults are at a low 0.6%.

AUTOMATION


FRAUD PREVENTION

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AI-powered risk management for financial institutions2 is embodied in advanced fraud prevention and AML solutions as well as more accurate customer assessment. In the United States, one of the most interesting startups on the radar of financial institutions allows institutions to identify and eradicate fraud in all avenues of commerce including online and in-person banking in real time. Through its continuous and rapid evaluation of large amounts of data, the company can conduct large-scale analyses, and fraudulent or questionable activity is identified and the customer is rapidly alerted. The service also assists payment providers and retailers in monitoring and protecting financial activity in connection with their companies.Lloyds Banking Group has partnered with AI startup Pindrop to use its machine learning technology to detect fraudulent phone calls3. Pindrop can identify 147 different features of a voice from a phone call or even a Skype call, which can help a person identify information such as the caller’s location, creating an “audio fingerprint.” Lloyds Banking Group will introduce the software across the Lloyds Bank, Halifax, and Bank of Scotland brands. Lloyds said the partnership with Pindrop will help it cut down call times as well as protect customers.Institutions in Europe have also begun using AI to improve their fraud prevention. HSBC, for example, is bringing in robots to help it spot money laundering, fraud, and terrorist funding4. The bank is planning to integrate the AI software of Quantexa, a UK-based startup, to screen the vast amounts of data it holds on customers and their transactions against publicly available data, in the search for suspicious activity.2https://medium.com/all-technology-feeds/spotlight-on-the-remarkable-potential-of-ai-in-kyc-7441bf7eec383 https://www.cnbc.com/2016/10/11/lloyds-uses-google-backed-ai-to-detect-phone-fraudsters.html4https://www.ft.com/content/b9d7daa6-3983-11e8-8b98-2f31af407cc8


Another European bank has partnered with a startup and implemented its enterprise analytics solution using AI to better identify instances of fraud while reducing false positives. Through this, the bank reduced 60% of false positives and increased true positives by 50%.ING Bank went ahead with replacement of a traditional rules-based anomaly detection system with one powered by machine learning algorithms. Previous testing has shown this will improve performance significantly –much more than the 5%-10% typically attained in a technology upgrade.In the APAC region, AI has a more defining, and more rarely discussed impact – stress testing result submissions and the adjustment of capital requirements for institutions. Regulatory bodies are interested in adopting advanced technologies to tackle the issue. The Financial Stability Board (FSB) recently published a report5 sharing that some regulators are using AI for fraud and AML/CFT detection.

“The Australian Securities & Investments Commission (ASIC) has been exploring the quality of results and potential use of NLP technology to identify and extract entities of interest from evidentiary documents. ASIC is using NLP and other technology to visualize and explore the extracted entities and their relationships. In order to fight criminal activities carried out through the banking system (such as money laundering), BdI collects detailed information on bank transfers and correlates this information with information from newspaper articles. The correlation involves both structured and unstructured data for file sizes of more than 50 gigabytes.”

5http://www.fsb.org/2017/11/artificial-intelligence-and-machine-learning-in-financial-service/

FRAUD PREVENTION


ASIC has also used machine learning (ML) software to identify misleading marketing in a particular subsector, such as unlicensed accountants in the provision of financial advice.FSB also shares an example of the Monetary Authority of Singapore (MAS) exploring the use of AI and ML in the analysis of suspicious transactions to identify those transactions that warrant further attention, allowing supervisors to focus their resources on higher-risk transactions.

“Investigating suspicious transactions is time-consuming and often suffers from a high rate of false positives due to defensive filings by regulated entities. Machine learning is being used to identify complex patterns and highlight the suspicious transactions that are potentially more serious and warrant closer investigation. Coupled with machine learning methods to analyze the granular data from transactions, client profiles, and a variety of unstructured data, machine learning is being explored to uncover non-linear relationships among different attributes and entities, and to detect potentially complicated behavior patterns of money laundering and the financing of terrorism not directly observable through suspicious transactions filings from individual entities.”

One of the largest institutions in Australia is developing AI technology to help its cybersecurity, fraud detection, and regulatory compliance functions.

At the end of January, HDFC Bank had completed a pilot for AI-based Cyber Security Operations Centre (CSOC) and the bank is slated to go live with it soon. The log data from CSOC is to be sent for processing on the AI solution having big data capabilities and it was done for around eight months on a cloud platform. The bank has close to 100,000 employees and the AI solution is expected to help in monitoring insider threats.

Partnerships are not unique to the goal of developing a virtual assistant. OCBC Bank, for example, partnered with ThetaRay to use its AI solution to identify potentially suspicious transactions. The solution has reduced the volume of transactions reviewed by AML compliance analysts by 35%, and increased the accuracy rate of identifying suspicious transactions by more than 4X.

FRAUD PREVENTION


CYBERSECURITY, COMPLIANCE, INTERNAL INTEGRITY

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As for compliance applications, an Australian institution has used software that uses NLP and AI to convert 1.5 million paragraphs of MiFID regulation into actionable tasks as a pilot program.

One of the largest financial institutions in the US has introduced its own intelligent platform designed to analyze legal documents and extract important data points and clauses. The use of AI reduced the review of 12,000 annual commercial agreements from hundreds of thousands of man-hours to a few seconds. The program, developed by the bank, does the job of interpreting commercial-loan agreements that, until the project went online in June 2016, consumed 360,000 hours of work each year by lawyers and loan officers. The software reviews documents in seconds and is not prone to errors.

The same firm set up technology hubs for teams specializing in big data, robotics, and cloud infrastructure to find new sources of revenue while reducing expenses and risks. The system already is helping the bank automate some coding activities and making its 20,000 developers more productive, saving money.

Cybersecurity, closely tied to standards of the regulatory framework in the market, is no longer a financial liability. Rather, it’s a competitive advantage of the modern day.


To bring the capabilities of intelligent machines into risk management, TD Bank, for example, acquired an AI startup Layer6. Layer6 team has built an AI prediction engine that can ingest a variety of data types – customer profiles, transaction histories, phone calls, images (photos and documents), and video – and be trained to make predictions, such as what the next best action for a customer is. The more important part is that it can also be applied to detecting fraud, scoping out cybersecurity threats and underwriting loans.Being one of the most forward-thinking institutions, Goldman Sachs, for example, has strong ties (as a customer and as an investor) with an AI software provider Digital Reasoning, whose solution GS uses to track traders6.The same startup has also launched a program with NASDAQ to use its AI technology to track trading data, communications, emails, chats, and even voice data to ferret out misconduct across the entire electronic stock exchange.AI software has also been shown to improve companies’ straight through reconciliation of incoming payments. Aiming at large or complex companies that are seeking to reduce costs, decrease days sales outstanding (DSO), and improve cash forecasting & their end-customer experience, one of the solutions in the market is suited for companies that manage a large volume of payments & e-invoices, where the remittance information is either missing or received separately from the payment. The tool identifies payers and associates’ payments to remittances that are received separately; extracts remittance data from emails, email attachments, electronic data interchange (EDI) and payer web portals; matches e-invoices to open receivables using the enriched remittance data and creates a receivables posting file that the client uploads to their ERP system.6http://www.forbes.com/sites/antoinegara/2016/11/07/wall-streets-big-brother-the-startup-goldman-sachs-and-steve-cohen-are-using-to-track-traders/#4138c6e3b13e

CYBERSECURITY, COMPLIANCE. INTERNAL INTEGRITY


A tool developed by one of the largest European institutions systematically screens contracts for compliance purposes. It takes 15 seconds to screen 150 pages, and the tool makes it possible to identify the names of legal entities, people, locations, vessels, etc.HSBC is using AI to predict how customers might redeem their credit card points so it can market its rewards offerings more actively and effectively. The rewards program will read customer data to predict how they might redeem their credit card points so it can market the offerings of a certain category – travel, merchandise, gift cards or cash – more actively.

CYBERSECURITY, COMPLIANCE. INTERNAL INTEGRITY


AUTHENTICATION,UNDERWRITING

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ANZ, an institution in the APAC region, has added a voice ID function to its Grow by ANZ app, allowing customers to authorize fund transfers of more than $1000 and BPAY payments of more than $10,000 without a PIN or password. The bank intends to roll the technology out to other digital channels. The service uses technology from Nuance, which also provides voice biometrics technology to the Australian Taxation Office.Speaking of biometrics, India’s largest bank has developed an AI-powered solution which scans cameras installed in branches and captures the facial expressions of customers and immediately reports whether the customer is happy or sad. The bank plans to build a dashboard using the solution that will gauge the effectiveness of representatives.Meanwhile, the banks’ wealth division is exploring AI opportunities to improve its underwriting processes. The bank has collaborated with University of Technology Sydney’s Advanced Analytics Institute to develop underwriting model that will harness ML to provide an opportunity for insurers to develop more efficient and reliable assessment processes.


“With exponential growth in computing power and favorable supply-side factors, like the advent of advanced algorithms, a vast pool of indigenous AI-related talent and massive government-funded infrastructure development, both the breadth and depth of AI adoption in Asia is set to sharply accelerate in the coming decade.”

Min Lan Tan, Group Managing Director, Head APAC Investment Office, CIO Wealth Management, UBS

“I think Asia is extraordinarily well-prepared to lead in the Cognitive Era, and here’s why: the pioneers of this era, the people that are actually bringing cognitive solutions to different industries, are the app developers, the data engineers, and scientists, and Asia has these skills in spades. India is now home to one of the largest populations of developers in the world.

Just as important, many countries in the region, like Singapore, Australia, and Korea, are investing in initiatives to support their developer communities and the broader cognitive economy. In fact, last year, the Korean government announced it would spend KRW 1 trillion (about USD 884 million) by 2020 to boost the artificial intelligence industry, which is part of the reason why Watson is now ‘learning’ Korean.”

Dr. John E. Kelly III, SVP, Cognitive Solutions & Research, IBM

ADVISORYIn the advisory function, ANZ used IBM Watson to help its financial advisors understand its clients better by providing tailor-made services. The bank is now expanding its use into areas such as risk and back-office automation.



VIRTUAL ASSISTANTS,

CHATBOTS

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Finally, the one use case that has seen the most significant interest, media coverage, and development, is the area of consumer interactions – the front-end, encompassing applications in credit scoring, insurance, and client-facing chatbots. In July 2017, Wells Fargo became the first US bank with a Facebook Messenger chatbot. This virtual assistant communicates with users to provide account information and helps customers reset their passwords.

Another bank – Bank of America – launched an intelligent virtual assistant named Erica7 which is a chatbot leveraging “predictive analytics and cognitive messaging” to provide financial guidance to the company’s over 45 million customers. It will be accessible to clients 24/7 and perform day to day transaction, understand their unique financial needs and help them reach their financial goals by providing smart recommendations.

A large NY-based institution is building a voice-controlled artificial intelligence platform that can help the firm’s IT staff manage enterprise storage. The AI chatbot is expected to be available to thousands of IT employees in an effort to help them automate manual storage-related tasks. IT employees will be able to communicate by voice or text with the chatbot, integrated with the enterprise collaboration tool Skype for Business, on a desktop.

7http://newsroom.bankofamerica.com/files/press_kit/additional/2016_Money2020_BAC_Announcement.pdf


“The biggest focus of banks with their AI initiatives is conversational interfaces and virtual assistants, closely followed by process excellence and fraud detection. While a relatively fewer number of banks are looking at AI in compliance and trading functions, there is no shortage of opportunities to be explored in these spaces. As the year 2018 unfolds, we may expect several new acquisitions/partnerships by banks and AI startups. These partnerships will play a major role in defining the perimeter for the evolving AI strategies of banks.” – Diwakar Mandal, MEDICI

To mention a few examples of European Institutions – Russia-based Tochka Bank has launched a Facebook bot for a range of financial services that include allowing the bank’s clients to check their accounts, finding nearby ATMs using geolocation, calling the bank function, contacting customer support, and making payments via Facebook messenger.

Another major institution has integrated IBM Watson and has designed a customer service chatbot – a natural language processing AI bot to answer customers’ questions and perform simple banking tasks like money transfers. If the bot is unable to find the answer it will pass a customer over to a member of staff.

A London-based bank has deployed a text-based chatbot, which customers can use on the banks’ online help pages. The chatbot can answer 200+ basic banking queries.One of the largest banks in the Pacific has launched a chatbot app that helps customers with more than 200 banking tasks such as activating their card, checking the account balance, making payments, or getting cardless cash.

VIRTUAL ASSISTANTS, CHATBOTS


Another institution launched a digital virtual banker specifically for business customers, enabling them to receive instant answers and assistance with common banking questions and tasks. The banks’ virtual banker is in pilot mode and available 24/7, providing help with more than 200 common questions related to the servicing of business banking accounts.The virtual assistant’s AI is derived from thousands of real-life customer inquiries. There are more than 13,000 variants of the 200 questions the virtual banker can answer; if the question can’t be answered, the customer will then be directed to a human banker. What’s important is that customers were involved in the testing and development phase.Meanwhile, one of the largest banks in Southeast Asia is using an AI-powered virtual assistant to enhance the experience at its mobile-only bank in India. An assistant that continuously learns can understand language the way humans speak it, helping the mobile-only bank to anticipate and reply to thousands of customer queries, and customers to fulfill banking transactions in real-time, at any time, anywhere.Furthermore, one of the largest institutions in Southeast Asia has launched AI-powered voice banking in collaboration with Google. Anyone can use the service by speaking to the Google Assistant – on a smartphone or a Google Home device – to initiate a conversation about the banks’ services. These services range from planning for retirement or a new home to saving for a child’s education, getting the latest financial market updates, and more.While some institutions use own resources to develop assistants, others find tech partners to power the function. YES BANK & PayJo, HDFC Bank & Senseforth.ai, HDFC Bank & OnChat, Axis Bank & Active.ai, and OCBC & CogniCor, for example.



YES BANK has also partnered with Gupshup to launch YES mPower, a banking chatbot that helps customers get information about loan products offered by YES BANK and instantly gives loan eligibility. YES BANK plans to incrementally release more services through the chatbot, making YES mPower the one-stop shop for all loan requirements.The State Bank of India is another interesting case: The Silicon Valley-based startup Payjo has launched an Intelligent Assistant for SBI, one of the world’s largest banks with 420 million customers. SBI’s Intelligent Assistant or SIA, an AI-powered chat & voice assistant, answers customer inquiries instantly and helps customers with everyday banking tasks. SIA is set up to handle nearly 10,000 inquiries per second or 864 million per day – close to 20% of Google’s worldwide traffic. SIA continuously learns with each interaction and gets better over time. Currently, it can answer inquiries on banking products and services and will soon be able to process financial transactions in many Indian regional languages across multiple customer channels in both voice and text format.



IB, TRADING, WEALTH MANAGEMENT

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Investment banking and trading have been somewhat controversial in yielding results when AI was brought into space8, although continuously hold a lot of promise. For example, hedge funds that use artificial intelligence and machine learning in their trading process posted the worst month on record in February 20189, according to a Eurekahedge index that’s tracked the industry from 2011. While computerized programs are feared for their potential to render human traders obsolete, the AI quants lagged behind their discretionary counterparts.

Investment banking and trading, nonetheless, are some of the most important and interesting areas with high stakes. The very same institutions that are exploring the capabilities of AI in automation and regulatory functions have also been experimenting with using ML in investment banking. One of the institutions in the US last year launched a predictive recommendation engine to identify clients that should issue or sell equity. After some initial success, the bank is expanding this into other areas such as debt capital markets.The same bank has also partnered with a tech company to deploy a data analytics and ML program in the bank’s fixed-income sales and trading operations. The solution is supposed to compile data from all desks and orders to give salespeople and traders a clearer picture in real time and help them anticipate market moves.

8 https://www.bloomberg.com/news/articles/2018-03-12/robot-takeover-stalls-in-worst-slump-for-ai-funds-on-record9 https://www.bloomberg.com/news/articles/2018-03-12/robot-takeover-stalls-in-worst-slump-for-ai-funds-on-record


Wells Fargo analysts, for example, built a robot called AIERA (artificially intelligent equity research analyst), which is now tracking 13 stocks10.“AIERA’s primary purpose is to track stocks and formulate a daily, weekly and overall view on whether the stocks tracked will go up or down,” said Ken Sena, Managing Director, Global Internet Analyst, Wells Fargo Securities. “View AIERA as enhancing versus replacing.”The months spent developing the bot helped the team of analysts deepen their understanding of the artificial intelligence and machine learning capabilities used by many of the internet companies they analyze. While AIERA is not picking stocks in the traditional sense yet, her validity tests continue to indicate above average.Wealth management has also seen the adoption of advanced technologies in the face of robo-advisors. In 2016, assets managed byrobo-advisors11 were estimated to increase by 68% annually and reach~$2.2 trillion12 in five years. Other estimations suggested that robo-advisors will be managing $8 trillion globally by 202013.Morgan Stanley’s wealth management unit is testing the AI/ML upgrade to its “next-best-action” system from rule-based approaches to suggesting investment options, to a system that employs machine learning to match investment possibilities to client preferences and helps its 16,000 financial advisors. The next-best-action system also enables automated alert generation on events such as margin call, low balance, significant increase/decrease in portfolio, Brexit impact, etc. With the debate on humans vs. robo-advisors finally being over14, this application empowers FAs to better serve their customers by setting up personalized messages with alerts. Morgan Stanley’s system also sends recommendations based on life events. For example, a client had a child with a certain illness; the system could recommend the best local hospitals, schools, and financial strategies for dealing with the illness. Morgan Stanley plans to eventually release a digital-only version with managed portfolios. It will be offered at a lower cost level to clients who prefer digital-only channels.10 https://www.bloomberg.com/news/articles/2017-09-27/wells-fargo-research-analysts-invent-their-own-ai-replacement11https://letstalkpayments.com/at-least-20-robo-advisors-can-leave-a-human-unemployed/12 https://letstalkpayments.com/sector-summary-on-robo-advisors-future-of-wealth-management/13 http://www.businessinsider.com/the-2-trillion-opportunity-for-robo-advisors-2016-614 https://gomedici.com/debate-finally-ends-robo-advisors-vs-humans/



One of the largest European banks launched an artificial intelligence bond trading tool that will help human traders to swiftly gather better bond prices. The tool will use data from hundreds of thousands of trades to help the bank’s traders to get better bond prices faster. In a six-month trial, the AI tool led to faster pricing decisions for 90% of trades and cut trading costs by 25%.UBS, for example, is working on solutions using machine learning to develop new strategies for trading volatility on behalf of clients. It scans vast amounts of trading data and creates a strategy based on learning from market patterns. The strategy, however, has then to be approved by human employees. The bank is also developing an AI tool for investment research. It can screen through market data through SEC filings and can actually do a company valuation with all of the inputs that a human analyst would use and can produce text in a fairly decent quality and almost human-mimicking language.Deutsche Bank has rolled out new AI-based equities algorithmic platform in APAC, which was designed with a self-learning mechanism allowing its systems to predict equities pricing and volume with more accuracy, thereby enhancing the quality of execution. This was added as a capability to its Autobahn platform.

“Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry.” – How Banks Are Using AI as a Tool for Transformation15


15https://gomedici.com/how-banks-are-using-ai-as-tool-for-transformation/


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