November 29, 2017
Annual worldwide AI revenue is projected to grow from $644 million in 2016 to $37 billion by 2025, with top use cases including algorithmic trading strategy performance improvement; static image recognition, classification, and tagging; efficient, scalable processing of patient data; predictive maintenance; content distribution on social media; and more.
The financial services industry is no stranger to machine learning – a number of large institutions continue to successfully implement the technology across such areas as risk analytics and regulation, customer segmentation, cross-selling and upselling, sales and marketing campaign management, creditworthiness evaluation. Among institutions that are applying machine learning are BBVA, JPMorgan Chase, HSBC, OCBC, and many more.
Credit applications and underwriting are the key areas where machine learning, and data analytics in general, will have an initial impact. The outcomes will include cost reductions, increased efficiency, and less onerous customer experiences, experts suggest.
McKinsey reports that in Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10% increases in sales of new products, 20% savings in capital expenditures, 20% increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built micro-targeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene, the consultancy says.
Let’s explore some interesting examples of machine learning applications in banking.
Cristóbal Sepúlveda, Technical Architect at BBVA, exposed an actual use case of this technology:
At BBVA, we developed a service recommendation engine for bank users. With this proposal, what we are trying to do is offer the best commercial offer depending on the most used transactions by the user and their navigation patterns. All this information is processed in a classification algorithm which then generates a recommendation.
The volume of information is incredibly vast and the only way to offer a recommendation is using machine learning technologies, he noted.
Read more on how BBVA embraces artificial intelligence and machine learning, in particular.
At JPMorgan Chase, a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN (Contract Intelligence), 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, is less error-prone, and never asks for vacation.
Made possible by investments in machine learning and a new private cloud network, COIN is just the start for JPMorgan Chase. The 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. When needed, the firm can also tap into outside cloud services from Amazon, Microsoft, and IBM. Read more.
The CIO at HSBC Darryl West said the bank is using machine learning to run analytics over this huge data set with great compute capability to identify patterns in the data to bring out what looks like nefarious activity within our customer base. The patterns that we identify are then escalated to the agencies and we work with them to track down the bad guys.
The bank said that it is using Google Cloud machine learning capabilities for AML. Read more.
The Singapore-based OCBC Bank has unveiled plans to use artificial intelligence and machine learning as part of its efforts to reduce financial crimes. The bank intends to deploy these technologies to deal with the increasing scale and complexity of AML monitoring, in addition to increasing the bank’s operational efficiency and accuracy in the detection of suspicious transactions. OCBC Bank has conducted a PoC with ThetaRay. Now, the company plans to start an extended PoC and a pre-implementation phase. The algorithm will detect anomalies in transactional behavior by evaluating broad parameters such as products, customers, and risks, instead of looking at each transaction as a standalone. In the PoC stage, the technology was deployed to analyze one year’s worth of OCBC Bank’s corporate banking transaction data. The findings demonstrated that it decreased the number of alerts, which did not require further review, by 35%. Read more.
Lloyds Banking Group has partnered with AI startup Pindrop to use its machine learning technology to detect fraudulent phone calls. 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 location that a caller is in 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.
The reason for us doing it is about saving money from fraud, said Martin Dodd, Group Telephone Managing Director at Lloyds Banking Group.
Read more about how Lloyds uses Google-backed AI to detect phone fraudsters.
Danske Bank, the largest bank in Denmark, has created an in-house startup called Advanced Analytics, whose sole purpose to use machine learning for predictive models to assess customer behavior and preferences on a personal level.
By analyzing customer data, we were able to identify the customer’s preferred means of communication, such as phone, letter or email. [This sort of valuable info] has helped improve our marketing campaign hit rate by a factor of four, says Bjørn Büchmann-Slorup, Head of Advanced Analytics at Danske Bank. Read more.
Bank of America Merrill Lynch announced a new solution in August 2017 called Intelligent Receivables, which uses artificial intelligence and other software to help companies improve their straight-through reconciliation (STR) of incoming payments to help them post their receivables faster.
Our solution brings together AI, machine learning and optical character recognition (OCR), setting a new bar in accounts receivable reconciliation and payment matching, added Gardner. We’re excited to be working with leading FinTech provider HighRadius to add Intelligent Receivables to our suite of solutions.
Bank of America Merrill Lynch’s Intelligent Receivables solution, powered by HighRadius’ cutting-edge machine-learning technology, will enable their corporate clients to accelerate the adoption of electronic payments from their end-customers. We are extremely excited to work with BofA Merrill on modernizing treasury management services and streamlining the receivables-to-cash cycle, said Sashi Narahari, CEO & President of HighRadius Corporation.
Read more in the official press release.
SEC turned to advanced methods after the 2008 crisis. …the use of simple word counts and something called regular expressions, which is a way to machine-identify structured phrases in text-based documents. In one of our first tests, we examined corporate issuer filings to determine whether we could have foreseen some of the risks posed by the rise and use of credit default swaps [CDS] contracts leading up to the financial crisis. We did this by using text analytic methods to machine-measure the frequency with which these contracts were mentioned in filings by corporate issuers. We then examined the trends across time and across corporate issuers to learn whether any signal of impending risk emerged that could have been used as an early warning.
Until today, SEC actively studies the potential of machines learning through continuous testing across core activities.
FINRA monitors roughly 50 billion market events a day, including stock orders, modifications, cancellations, and trades. It looks for around 270 patterns to uncover potential rule violations. It would not say how many events are flagged, or how many of those yield evidence of misbehavior. The machine learning software FINRA is developing will be able to look beyond those set patterns and understand which situations truly warrant red flags.
More on how FINRA is leveraging machine learning and artificial intelligence to catch stock market cheaters can be found here.
LSE has teamed up with IBM Watson business and cybersecurity firm SparkCognition to develop its AI-enhanced surveillance, said Chris Corrado, Chief Operating Officer of LSE Group, in an interview with Reuters.
Wells Fargo analysts built a robot called AIERA (artificially intelligent equity research analyst), which is now tracking 13 stocks.
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 at 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. Read more.
The bank has been working on a project dubbed AppBank. The initiative seeks to use machine learning. AppBank is run by a new business unit, which includes data scientists and machine learning professionals. Its goal is to increase large-scale automation and while it is particularly focused on operations technology, it will tackle applications across every business unit at the firm.
The goal is to be able to provide more insight into the health and operations of the systems. We think of it as our ‘check engine light’ product, said Don Duet, Head of Technology at GS.
Like a light on a car dashboard coming on to indicate a problem, the software would inform users when there was something that could prevent the bank’s technology infrastructure from running smoothly. Read more.
Being one of the most forward-thinking institutions, Goldman Sachs has strong ties (as a customer and as an investor) with AI software provider Digital Reasoning, whose solution GS uses to track traders. 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. Goldman Sachs also uses the machine learning platform Kensho to mine data from the National Bureau of Labor Statistics and compile all that information into regular summaries. The reports feature 13 exhibits predicting stock performances based on similar employment changes in the past, and they’re ready to print just nine minutes after the data is entered.
The areas where machine learning will have significant impact professionals emphasize risk management; compliance; financial crime, fraud detection, and cybersecurity; credit underwriting and portfolio monitoring; customer sales and service.
The Western Independent Bankers (WIB) shares that banks and FinTech companies already use machine learning to detect fraud by flagging unusual transactions. Such anomalies are investigated, with the result being fed back into the system so it can learn and thus further build the customer profile. The process is far more efficient than human manual monitoring and is expected to become the norm in banking and finance.
While previous financial fraud detection systems depended heavily on complex and robust sets of rules, modern fraud detection goes beyond following a checklist of risk factors – it actively learns and calibrates to new potential (or real) security threats. This is the place of machine learning in finance for fraud – but the same principles hold true for other data security problems. Using machine learning, systems can detect unique activities or behaviors (anomalies) and flag them for security teams. The challenge for these systems is to avoid false-positives – situations where risks are flagged that were never risks in the first place, Tech Emergence emphasizes.
Source: Demystifying Machine Learning for Banking, Feedzai
In a tough banking environment, banks are looking to machine learning to reduce costs and increase retention. Research suggests that banks that have replaced older statistical-modeling approaches to credit risk with machine learning techniques have experienced up to 20% increases in cash collections from outstanding loans.
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