The long, slow journey towards true customer-centric banking

Banks are working to improve their customer experience. The future of banking will be more than just a digital facelift and will include proactive use of consumer data and cognitive computing to sell to and service the customer.

Banking executives report that customer experience and mobile banking are the main drivers behind software investments they plan to make during the next 18 months, according to The State of Retail Banking 2016. Consumers, on their part, are giving banks higher grades on customer experience, according to Capgemini’s World Retail Banking Report 2016

Is that enough? According to Capgemini, even with the increase in customer experience satisfaction, profitable customer behavior increased only marginally.

With the advance of cognitive computing and analytics, banks have the opportunity to deepen their relationship with customers and drive more profits.

“A lot of banking CMOs are giving lip service to digital banking,”said Martin Häring, CMO of Misys, a banking software company. “Many banks say ‘we are customer-centric’, but in effect they are not.”

Häring envisions a world in which banks leverage transactional, behavioral and even location data to touch the customer with the right message at the right time, and proactively help the customer take control over his financial decisions.

Banks can use data to predict customer needs and proactively cross-sell products in a way the customer will appreciate. For example, software can warn a customer his expenses are higher this month, or perhaps, if expenses are lower, offer a deal on a product the customer needs, based on his purchase history.  

“What we we think of as digital banking today is actually just digitized banking,” explains Jason Bates of 11FS in a blog post. “In the same way that putting a copy of a newspaper onto an iPad is not digital news, selling albums on iTunes is not digital music, and getting fast access to taxi numbers is not digital transportation, looking at a digital copy of your paper statement and account balance on your iPhone just doesn’t cut it any more.”

Banks face many challenges in developing new products, like aging core banking systems and data or organizational silos that restrict innovation. That’s not to say nothing is being done. Efforts to launch customer-centric product are underway.

Though some banks will try to develop next generation banking apps in-house, most will turn to partnering with startups and leveraging open APIs to provide a truly personalized customer experience that empowers users.

Banks will turn to companies like Misys or Strands to power their personalization efforts. Other companies, such as Ayasdi, or IBM’s Watson, provide banks with AI or cognitive computing abilities that can better leverage consumer data.

It took newspaper almost two decades to move from online text and photos to a full digital experience that includes interactives, video and infographics. Similarly, even with some experiments in customer-centric banking, it will take banks some time to fully deliver on the promise of digital banking.

 

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How Ayasdi’s machine learning is giving banks an analytical advantage

automating Wall Street, fintech, and layoffs

As the famous adage goes, “the future is already here — it’s just not very evenly distributed.” This is especially true for banking and the use of machine learning. Banks are increasingly using machine learning to power part of their operations, but the adoption of these new technologies is not uniform. The road to full implementation is fraught with technological and organizational challenges.

Machine learning is a collection of techniques for understanding data, including methods for visualization, prediction, classification and other tasks relevant to data analysis. Banks are implementing machine learning in three main stages. The first stage is automating manual processes that are currently handled by an army of quants, such as risk modeling, stress testing or Anti-Money Laundering monitoring.

Top data scientists are employed by financial institutions and working with programming tools like SPCC and R, they filter and analyze huge data sets in order to perform analytical tasks. “No human being can wrap his head around that amount of data,” said Daniel Druker, CMO of Ayasdi, a machine learning company that partners with financial institutions, like Citi and Credit Suisse.

Instead, using machine learning algorithms, a computer can surface insights and recommendations from those data sets, while the quants examine and take actions based on those learnings.

“It is not a person guessing what part of the data set is relevant. With machine learning, 5 thousand man hours go down to an afternoon,” Druker explained.

For AML, human beings currently monitor and check suspicious transactions, which is very time consuming. With machine learning, however, a computer can use historical data to filter out the easy cases, leaving the hard cases for humans to analyze.

According to McKinsey’s 2015 Global Banking Report, banks that have replaced older statistical-modeling approaches to credit risk with machine learning techniques have experienced up to 20 percent increases in cash collections from outstanding loans.

Out of over 20 banks that work with Ayasdi, Druker said, 100% are either already operating in this stage or actively exploring implementing such technology.

The second stage of implementing machine learning is blurring the line between humans and machines in areas such as customer support. With Natural Language Processing, a computer can easily answer the majority of customer questions, leaving humans to deal only with more complex customer service problems. Druker estimates about 50% of banks are exploring or have already implemented such applications.

The highest level of machine learning application is the fully automating business processes. Take a life insurer, for example. When a customer applies for a policy, he might be asked to fill out a 40-page long form and get a physical examination. That information is then sent back to the company for approval. The entire process can take over a month to complete.

With a fully automated service, a computer can use personally-identifying information about a prospect to search everything it knows about him, including credit scores, purchase history, social media profiles. It can then instantaneously approve an application.

Good analysis of customer data, using advanced analytics techniques, can help banks further personalize their offerings to clients, increasing customer loyalty and boosting revenues.

According to McKinsey, some European banks using these techniques report 10 percent increases in sales of new products, 20 percent savings in capital expenditures, and 20 percent declines in customer churn.

“Banks know this is coming, but these are still board room discussions,” Druker said.

IP Soft, a company that automates IT and business processes for enterprises across a wide range of industries, recently published a white paper on a fully AI-enabled bank. Not unlike Druker’s explanation, this chart from the report offer a nice summary.

screenshot-www.ipsoft.com 2016-07-27 13-03-12

In a tough banking environment, banks are looking to machine learning to reduce costs and increase retention.

Smaller banks with less resources are understandably having harder time implementing machine learning solutions. According to Capgemini’s report, “Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?”, silos across business lines or data distributed across different systems such as CRM, portfolio management and loan servicing, hamper proper integration.

“Banks lack a seamless 360-degree view of the customer,” claimed the report. “Further, many banks have inflexible legacy systems that impede data integration and prevent them from generating a single view of the customer.”

A common pushback from banks is regulatory concern. Machine learning applications for banking are uncharted territory and neither banks nor regulators fully know yet how to handle them yet. There is a lot of work and discussions currently underway.

In addition, these tools threaten the jobs of people who traditionally handled analytic tasks for banks. “Nobody want to fire all the quants,”Ayasdi’s Druker said. Forward-looking banks see that with the integration of machine learning, their top talent will be freed up to spend time on more value-added tasks for the organization.

CB Insights has identified 41 companies providing machine learning solutions in the financial industry. Together with the explosion of general applications of  AI, deals and investments in AI companies reached record levels in 2016. Since the beginning of 2016, over 15 fintech AI companies have closed investment rounds.

Among the main areas of interest are analytics, assistance bots, market research and credit scoring.

AI fintech

 

OTAS Technologies’ Tom Doris is creating machines to do (part of) a trader’s job

interview with fintech investor, Dan Ciporin

Tom Doris is CEO of OTAS Technologies

What’s OTAS all about?

Tom Doris, OTAS Technologies
Tom Doris, OTAS Technologies

At OTAS we use big data analytics, machine learning and artificial intelligence to extract meaning from market data and provide traders and portfolio managers with insights that would otherwise lay hidden. Our decision support tools help traders to focus on what’s important and interesting, you could say that we use machines to identify the areas that humans should be paying attention to.

I did my Ph.D. in artificial intelligence, and around 2009, it was clear to me that several of the more sophisticated hedge funds were converging on a set of approaches to market data analysis that could be unified and made more efficient and general by applying algorithms from machine learning and artificial intelligence.

Better yet, it quickly became clear that the resulting analysis could be delivered to human traders and portfolio managers using natural language and infographics that made it easy to absorb and action. At the same time, the role of the trader was becoming increasingly important to the investment process, while the problem of executing orders was becoming more difficult due to venue fragmentation, dark pools, and HFT, so it was clear to me that there would be demand for a system that helped the trader overcome these problems.

How does leveraging artificial intelligence for trading help traders and portfolio managers make better decisions and manage risk?

Experienced traders and PMs really do have skill and insight. With all human skills, it is not easy to apply the skill systematically. We can leverage AI to help humans scale their investment process to a larger universe of securities, and also to ensure they apply their best practices on every single trade.

In many professions, everyday tasks are too complex for a human to execute reliably, for instance, pilots and surgeons both rely on extensive checklists. Checklists aren’t sufficient in financial markets because hundreds of factors can potentially influence a trader’s decision, so the problem is to first find the factors that are unusual and interesting to the current situation. This is the task that AI is exceedingly good at, and it’s what OTAS does. Once we’ve identified the important factors for a given situation, machine learning and statistics help to quantify their potential impact to the human, and we use AI to generate a natural language description in plain English.

What is compelling the increased use of artificial intelligence and big data analysis in financial services?

A basic driver is that the volume of data that the markets generate is simply too much for a human to analyze, but the more compelling reason is that AI and machine learning are effective and get the results that people want. Intelligent use of these techniques gives you a real edge in the market, and that goes to the firm’s bottom line.

How do you see artificial intelligence and big data analysis playing a role in trade execution in the future? Any predictions for 2016?

AI is going to provide increased automation on the trading desk. Execution algorithms have already automated the task of executing an order once the strategy has been selected by a trader. Now we’re seeing a big push to automate the strategy selection and routing decision process. The next milestone will be to see these systems in wide deployment, and with it will come a shift in the trader’s role; traders will have more time to focus on the exceptional orders that really benefit from human input. Also, the trader will be able to drive the order book in aggregate according to changes in risk and volatility. Instead of manually modifying each order, you will simply tell the system to be more aggressive, or risk averse, and it will automatically adapt the strategies of the individual orders.

What’s the biggest challenge in acquiring new customers in your space?

Traders have largely been neglected in recent years as regards technology that helps them to make better decisions. Even when the benefits of a new tool are clearly established, it can be difficult for the trading desks to get it through their firm’s budget. Despite the recent hype around HFT and scrutiny of trading, there’s still a lag when it comes to empowering traders with the best information and tools to support them.

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