Data Wars to Dominate 2017


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It’s the start of 2017, and many people have already blogged their predictions for the year. I won’t repeat those predictions, as the future isn’t what it used to be, but I do find it interesting to look at the common themes across them all. The standout theme for me is that 2017 is the year of The Analytic.

Data analysis to be exact. Now you can get analysis paralysis if you dwell on this too long, but data analytics will be the fuel for everything else. Effective data analysis is core to being able to leverage artificial intelligence; data analytics will be the key to unlocking the internet of things; and data analytics is essential to chatbots, augmented customer experiences and enhanced services.

Think about it: How can you deliver a decent digital service if you don’t have the data to tell you what your customers want? This then becomes the essential challenge for all incumbent institutions as their customer data is often siphoned into silos. I know that for a fact, having spent 20 years trying to create bank enterprise data stores and services. Now some banks are beginning to wake up and embrace the data opportunity and threat but, for those who are comfortable with distributed data and no ability to analyze it effectively, here’s the hard truth: You will not survive.

I’ve believed this for a long time but, with each passing year, I am sounding the alarm bell louder and louder. After all, we have argued for decades that a consistent customer experience across channels is essential. We haven’t been able to deliver it, but we’ve tried. Now we are not even talking channels anymore, we are just talking about a digital foundation that everyone accesses through open marketplaces online. We have moved from a historical, closed and proprietary architecture to an open platform structure where everyone can plug and play. But how can they do that if the data is locked in old proprietary systems that are siloed and closed?

This is going to be a key conundrum for U.S. banks, which are arguing that the only person who can access customer data is the customer. That’s a great way to lock out third-party players, shut down the aggregators and block the open systems march. However, it strikes me as being like the king who has placed his army at the gates of the castle, while not noticing that the citizens are all leaving via the back door. What is the use of having a kingdom if there’s no one in it? And that is what will happen to banks that continue to have distributed data that cannot be leveraged.

The march of the fintech community, the regulator and the customer is towards easy, convenient, proactive and personalized financial providers. Those providers are increasingly like the Amazons of the world: they know their customers digital footprint and maximize their knowledge of that footprint to the hilt. In 2017, as we watch the progression of AI, machine learning, deep learning, chatbots and personalization, any bank that keeps its data locked up in a chastity belt is missing a trick.

Using Data Science to Combat Internal Fraud


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It’s no secret that fraud prevention is a hot button topic in banking, and an increase in internal cybercrime has spawned a new wave of regulations to prevent violations like money laundering and insider trading. One need look no further than the recent allegations of Wells Fargo’s cross-selling misconduct to see the potential for financial and reputational loss.

Banks have long used monitoring and data analysis technology to flag potential instances or transactions related to internal fraud. Now, data science is being used as a tool both to prevent and predict fraud on accounts before it occurs. Here’s how financial institutions are joining forces with data science innovators to help monitor internal behavior to prevent and predict fraud.

Detecting Suspicious Patterns
One of the major areas that companies are looking at is analyzing spending and transaction patterns to detect fraud. This means analyzing the payment and purchase history of each customer on a granular level, and determining if any of those transactions appear to be out of the ordinary. Data science is now pushing the envelope into analyzing these activities for targeted marketing of rewards programs or other products in the future.

In addition, companies like RedOwl are using data analytics to spot internal fraudulent patterns to prevent employee malpractice before it happens. The RedOwl Analytics platform is specifically designed to predict whether an employee is likely to commit certain acts such as insider trader trading or intellectual property theft. Instead of simply monitoring employee emails and messages, RedOwl goes a step further by detecting and analyzing abrupt shifts in communication patterns or behaviors. Behavior such as suddenly changing to different languages, an increase in external messaging or emailing outside of normal work hours are some of the behaviors that may predict fraud and that RedOwl Analytics takes into account.

Monitoring Transactions and Flagging Activity
After suspicious patterns have been detected, the next challenge for big data is to monitor or flag specific transactions in order to step in at the appropriate time. At what point is the likelihood of fraud great enough for bank management, regulators or law enforcement authorities to step in and investigate? Palantir is one of the big players in the space, working with big banks like JP Morgan Chase & Co. to help identify rogue traders, for example.

Such needles in the haystack are tough to find, and that’s why Palantir’s technology is so useful. The Palantir Anti-Fraud platform, which originates from data science technology designed for U.S. Intelligence services, initially monitors and flags attempted hacks into client accounts or ATMs. Today, Palantir’s software monitors a variety of activities to prevent internal fraud as well. This includes a combination of trading data, email communications and keywords used in company phone calls.

Fraud Prediction and Investigation
The key to minimizing financial and customer loss due to fraud is quick detection and resolution. But the challenge is not just to accurately predict fraudulent actors—it’s to investigate and intervene accordingly. That’s where big data companies like Splunk are stepping in, to aid banks in pivoting from monitoring suspicious activity to taking action. One of the unique advantages to Splunk software for fraud prevention is the ability to analyze data from disparate, siloed sources to better predict who may perpetuate fraud.

What Splunk’s anti-fraud software does is establish a risk profile baseline for certain user groups. It then applies statistical analysis to employee activities–stock trading for example–to determine if they are acting within the baseline risk profile. Users whose activities are seen as anomalies by Splunk are then able to be flagged for further monitoring and investigation. Alerts for these anomalies can then be configured in real-time, or over a certain period to further validate potentially fraudulent patterns. Once potential fraud is detected, investigators will then have access to historical data to quickly determine who is involved and what they might be trying to accomplish. Splunk and other fintech companies that use data science techniques are also trying to add another layer to fraud investigation, cross-referencing patterns with other users in the company to determine if that person is acting alone or could be part of a larger ring.

Unfortunately, as of today there is no silver bullet in technology or big data that could prevent each and every instance of internal fraud from taking place. But as fintech innovators like Splunk, Palantir and RedOwl continue to push the boundaries in making sense of big data, banks can at least be more proactive in countering fraud before it happens.