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Opportunities in Data Analytics Across Financial Services

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Opportunities in Data Analytics Across Financial Services

The First Step in Reaping the Benefits Is Understanding the Differences and Real-World Implementations

ByTed Bissell

Ted Bissell is Global Director of Digital Consulting at Axis Corporate. He has more than 30 years of experience helping financial institutions implement innovation. 

In today’s Fintech-driven world, big data is the new currency. As companies infuse more technology innovation into the financial services ecosystem, data analytics has made leaps in accuracy and sophistication — creating new realms of untapped opportunities to serve customers better, increase operational efficiencies and increase product reach.

Traditionally, banks and financial institutions had not given adequate priority to structuring their internal data, which limited opportunities to capitalize on analytics that would have delivered value within their companies. The data has existed for years, however, difficulties in bringing together the right software, services and technology prevented them from making sense of it all.

Times have changed. The industry has evolved, and new technologies have entered the market. This has given banks and financial institutions new techniques to maximize investments in data and realize significant ROI when establishing analytics as a core company value. This influx of technology has also accelerated how customers engage digitally with banks and financial organizations, providing them with more-sophisticated ways to consume financial services.

Through the application of analytics across financial services, businesses are may now use data to form better customer relationships, offer more complex products at greater scale and better compete in the market with offerings precisely targeted to match customer need within context. By intensifying the use of unstructured and live-data sources, the benefits can be realized even quicker.

Types of Data Analytics Used in Financial Services — and Real-World Examples

Within financial services, there are four classic types of analytics. Traditionally, the most popular among these had been descriptive and diagnostic, which focus on historical data — typically supporting human decision processes. The higher levels of analytics, predictive and prescriptive, project future outcomes and therefore lend themselves to spread across financial services. Here, machine learning and other types of artificial intelligence (AI) mean that companies and customers no longer must be in direct communication, allowing transactions to happen instantly—with no time for live human intervention.

Let’s break down the four types of analytics and show how they’re being applied in the real world.

  1. Descriptive: This type of analytics creates a summary of historical data and is useful for fraud-event tracking and risk assessment. Segmenting customers into specific types and relying on better, descriptive data helps companies deeply analyze their sales cycles. Advantages that exist within this category include the ability to better data mine, understand patterns and the chance to focus on past performance to create better future opportunities.

Real-World Example: Feedzai is a platform used by payment-card issuers to examine commerce transactions and patterns to lower credit card payment risk. The machine-learning engine tackles several data points than previously used in payments. This technology creates behavior signatures for merchants and individuals, allowing a robust risk score to be generated. This score serves an advisory role as an input to an issuer’s broader fraud detection platform, with the decision to approve or deny a transaction left to the card issuer.

  1. Diagnostic: When determining how to better reach a target audience, diagnostic analytics is most useful in understanding who the customer is. When executing advertising campaigns or improving customer service performance and customer relationship management (CRM) effectiveness, this type of data helps measure ROI and overall impact.

Real-World Example: Kasisto is a chatbot that engages financial services customers in conversation, permitting a better understanding of their needs. It targets suggestions for existing financial service product offerings and spots areas of interest to guide the structuring of future offerings.

  1. Predictive: With this type of analytics, the emphasis shifts toward decision-making and forecasting the future. Particularly for product strategy, marketing resource allocation and enhancing onboarding efficiencies, predictive analytics can automate decisions, and use patterns of past habits to predict future behavior. Outcomes here can help automate customer interactions, keeping up with customers who are already arriving on the scene with their own agents and bots.

Real-World Example: Hearsay, a software-as-a-service digital marketing platform, concentrates on synthesizing insights about customer engagement on social media channels — incubating the process for automating marketing tasks for financial services. Through the platform’s analysis, it can suggest which individuals a service provider should interact with based on their previous behavior, and what type of approach will elicit a positive response.

  1. Prescriptive: A key benefit of data analytics is the ability to automate processes and provide product suggestions for customers. When planning to rely on bots to formulate and answer customer questions, or act as a recommendation engine, prescriptive analytics are useful. This will increasingly play a role as customer bots begin to interact directly with an institution’s bot.

Real-World Example: Betterment is a machine-learning-based investment portfolio selecting platform targeting customers who traditionally would not hire a person or firm to handle this task. By providing investment suggestions for this customer, they are able to connect customers to products that best meet their investment goals by overlaying the many complex rules that play into a particular investment portfolio decision.

Better, Faster Outcomes

Companies have always had data to manage. By integrating the data analytics described here that rely on machine learning, automation and AI, financial services companies may eliminate unnecessary human interaction that can lead to overlooking a customer need. This progression of technology has forced organizational readiness, streamlined customer operations and boosted revenue streams by making it easy for a customer to choose a service earlier in the decision-making process.

As the financial services industry sees traditional human-to-human customer interactions give way to customer bots communicating with the institution’s application programming interface (API), only careful deployment of machine learning and cognitive computing will allow for effective real-time, on-the-spot decision making demanded by the pace of these interactions.

The result, in many cases, is the ability to rely on an AI-based platform, often personified as a customer-service bot, to provide better, faster outcomes that position financial services companies to competitively market themselves. No matter how compelling a human interaction may be in financial services, changes in the surrounding world will build bigger hurdles to climb. Leveraging data analytics, machine learning and AI gives financial institutions the essential tools to break down these barriers.

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Oil extends losses as Texas prepares to ramp up output

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Oil extends losses as Texas prepares to ramp up output 1

By Devika Krishna Kumar

NEW YORK (Reuters) – Oil prices fell for a second day on Friday, retreating further from recent highs as Texas energy companies began preparations to restart oil and gas fields shuttered by freezing weather.

Brent crude futures were down 33 cents, or 0.5%, at $63.60 a barrel by 11:06 a.m. (1606 GMT) U.S. West Texas Intermediate (WTI) crude futures fell 60 cents, or 1%, to $59.92.

This week, both benchmarks had climbed to the highest in more than a year.

“Price pullback thus far appears corrective and is slight within the context of this month’s major upside price acceleration,” said Jim Ritterbusch, president of Ritterbusch and Associates.

Unusually cold weather in Texas and the Plains states curtailed up to 4 million barrels per day (bpd) of crude production and 21 billion cubic feet of natural gas, analysts estimated.

Texas refiners halted about a fifth of the nation’s oil processing amid power outages and severe cold.

Companies were expected to prepare for production restarts on Friday as electric power and water services slowly resume, sources said.

“While much of the selling relates to a gradual resumption of power in the Gulf coast region ahead of a significant temperature warmup, the magnitude of this week’s loss of supply may require further discounting given much uncertainty regarding the extent and possible duration of lost output,” Ritterbusch said.

Oil fell despite a surprise drop in U.S. crude stockpiles in the week to Feb. 12, before the big freeze. Inventories fell by 7.3 million barrels to 461.8 million barrels, their lowest since March, the Energy Information Administration reported on Thursday. [EIA/S]

The United States on Thursday said it was ready to talk to Iran about returning to a 2015 agreement that aimed to prevent Tehran from acquiring nuclear weapons. Still, analysts did not expect near-term reversal of sanctions on Iran that were imposed by the previous U.S. administration.

“This breakthrough increases the probability that we may see Iran returning to the oil market soon, although there is much to be discussed and a new deal will not be a carbon-copy of the 2015 nuclear deal,” said StoneX analyst Kevin Solomon.

(Additional reporting by Ahmad Ghaddar in London and Roslan Khasawneh in Singapore and Sonali Paul in Melbourne; Editing by Jason Neely, David Goodman and David Gregorio)

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Analysis: Carmakers wake up to new pecking order as chip crunch intensifies

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Analysis: Carmakers wake up to new pecking order as chip crunch intensifies 2

By Douglas Busvine and Christoph Steitz

BERLIN (Reuters) – The semiconductor crunch that has battered the auto sector leaves carmakers with a stark choice: pay up, stock up or risk getting stuck on the sidelines as chipmakers focus on more lucrative business elsewhere.

Car manufacturers including Volkswagen, Ford and General Motors have cut output as the chip market was swept clean by makers of consumer electronics such as smartphones – the chip industry’s preferred customers because they buy more advanced, higher-margin chips.

The semiconductor shortage – over $800 worth of silicon is packed into a modern electric vehicle – has exposed the disconnect between an auto industry spoilt by decades of just-in-time deliveries and an electronics industry supply chain it can no longer bend to its will.

“The car sector has been used to the fact that the whole supply chain is centred around cars,” said McKinsey partner Ondrej Burkacky. “What has been overlooked is that semiconductor makers actually do have an alternative.”

Automakers are responding to the shortage by lobbying governments to subsidize the construction of more chip-making capacity.

In Germany, Volkswagen has pointed the finger at suppliers, saying it gave them timely warning last April – when much global car production was idled due to the coronavirus pandemic – that it expected demand to recover strongly in the second half of the year.

That complaint by the world’s No.2 volume carmaker cuts little ice with chipmakers, who say the auto industry is both quick to cancel orders in a slump and to demand investment in new production in a recovery.

“Last year we had to furlough staff and bear the cost of carrying idle capacity,” said a source at one European semiconductor maker, who spoke on condition of anonymity.

“If the carmakers are asking us to invest in new capacity, can they please tell us who will pay for that idle capacity in the next downturn?”

LOW-TECH CUSTOMER

The auto industry spends around $40 billion a year on chips – about a tenth of the global market. By comparison, Apple spends more on chips just to make its iPhones, Mirabaud tech analyst Neil Campling reckons.

Moreover, the chips used in cars tend to be basic products such as micro controllers made under contract at older foundries – hardly the leading-edge production technology in which chipmakers would be willing to invest.

“The suppliers are saying: ‘If we continue to produce this stuff there is nowhere else for it to go. Sony isn’t going to use it for a Playstation 5 or Apple for its next iPhone’,” said Asif Anwar at Strategy Analytics.

Chipmakers were surprised by the panicked reaction of the German car industry, which persuaded Economy Minister Peter Altmaier to write a letter in January to his counterpart in Taiwan to ask its semiconductor makers to supply more chips.

No extra supplies were forthcoming, with one German industry source joking that the Americans stood a better chance of getting more chips from Taiwan because they could at least park an aircraft carrier off the coast – referring to the ability of the United States to project power in Asia.

Closer to home, a source at another European chipmaker expressed disbelief at the poor understanding at one carmaker of how it operates.

“We got a call from one auto maker that was desperate for supply. They said: Why don’t you run a night shift to increase production?” this person said.

“What they didn’t understand is that we have been running a night shift since the beginning.”

NO QUICK FIX

While Infineon, the leading supplier of chips to the global auto industry, and Robert Bosch, the top ‘Tier 1’ parts supplier, both plan to commission new chip plants this year, there is little chance of supply shortages easing soon.

Specialist chipmakers like Infineon outsource some production of automotive chips to contract manufacturers led by Taiwan Semiconductor Manufacturing Co Ltd (TSMC), but the Asian foundries are currently prioritising high-end electronics makers as they come up against capacity constraints.

Over the longer term, the relationship between chip makers and the car industry will become closer as electric vehicles are more widely adopted and features such as assisted and autonomous driving develop, requiring more advanced chips.

But, in the short term, there is no quick fix for the lack of chip supply: IHS Markit estimates that the time it takes to deliver a microcontroller has doubled to 26 weeks and shortages will only bottom out in March.

That puts the production of 1 million light vehicles at risk in the first quarter, says IHS Markit. European chip industry executives and analysts agree that supply will not catch up with demand until later in the year.

Chip shortages are having a “snowball effect” as auto makers idle some capacity to prioritize building profitable models, said Anwar at Strategy Analytics, who forecasts a drop in car production in Europe and North America of 5%-10% in 2021.

The head of Franco-Italian chipmaker STMicroelectronics, Jean-Marc Chery, forecasts capacity constraints will affect carmakers until mid-year.

“Up to the end of the second quarter, the industry will have to manage at the lean inventory level,” Chery told a recent Goldman Sachs conference.

(Douglas Busvine from Berlin and Christoph Steitz from Frankfurt; Additional reporting by Mathieu Rosemain and Gilles Gillaume in Paris; Editing by Susan Fenton)

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Aussie and sterling hit multi-year highs on recovery bets

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Aussie and sterling hit multi-year highs on recovery bets 3

By Tommy Wilkes

LONDON (Reuters) – The Australian dollar rose to near a three-year high and the British pound scaled $1.40 for the first time since 2018 on optimism about economic rebounds in the two countries and after the U.S. dollar was knocked by disappointing jobs data.

The U.S. currency had been rising in recent days as a jump in Treasury yields on the back of the so-called reflation trade drew investors. But an unexpected increase in U.S. weekly jobless claims soured the economic outlook and sent the dollar lower overnight.

On Friday it traded down 0.3% against a basket of currencies, with the dollar index at 90.309.

The Aussie rose 0.8% to $0.784, its highest since March 2018. The currency, which is closely linked to commodity prices and the outlook for global growth, has been helped by a recent rally in commodity prices.

The New Zealand dollar also gained, and was not far off a more than two-year high, while the Canadian dollar rose too.

Sterling rose to $1.4009 on Friday, an almost three-year high amid Britain’s aggressive vaccination programme.

Given the size of Britain’s vital services sector, analysts say the faster it can reopen the economy, the better for the currency. Sterling was also helped by better-than-expected purchasing managers index flash survey data for February.

The U.S. dollar has been weighed down by a string of soft labour data, even as other indicators have shown resilience, and as President Joe Biden’s pandemic relief efforts take shape, including a proposed $1.9 trillion spending package.

Despite the recent rise in U.S. yields, many analysts think they won’t climb too much higher, limiting the benefit for the dollar.

“Our view remains that the Fed will hold the line and remain very cautious about tapering asset purchases. We think it will keep communicating that tightening is very far off, which should dampen pro-dollar sentiment,” said UBS Global Wealth Management strategist Gaétan Peroux and analyst Tilmann Kolb.

ING analysts said “the rise in rates will be self-regulating, meaning the dollar need not correct too much higher”.

They see the greenback index trading down to the 90.10 to 91.05 range.

U.S. dollar

Aussie and sterling hit multi-year highs on recovery bets 4

The euro rose 0.4% to $1.2134. The single currency showed little reaction to purchasing manager index data, which showed a slowdown in business activity in February. However, factories had their busiest month in three years, buoying sentiment.

The dollar bought 105.39 yen, down 0.3% and a continued retreat from the five-month high of 106.225 reached Wednesday.

(Editing by Hugh Lawson and Pravin Char)

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