Data is everywhere and its impact is tangible. So fundamental is data to our society that by 2245, half the world’s mass will consist of data and information. The quantity of data we’re dealing with now and the speed at which this quantity is growing are astonishing. Per day, each person generates 1.7 megabytes of data per second, or 146,8 gigabytes per day.  Central role for data A good example of this kind of data is email. Daily, we send 306,4 billion emails, which is an average of 40 emails per day per person. And while we send all these emails, Google is processing 3,5 billion search requests every day. All this data has a profound impact on the way we live. More importantly, data is playing an increasingly important role in the corporate domain.  Take Netflix for example. Not only do they know how to provide users with relevant suggestions on what to watch next, but they also save around $1 billion per year by using data for client retention. This seems like a challenge, but it could hardly be easier. In practice, all they do is analyze the data clients generate themselves. Put differently, the financial gains to be made with proper data use versus effort needed are enormous.  While a lot can be achieved using data, huge losses can also be sustained as a result of poor data management. A recent study showed that the American economy is losing approximately $3,1 trillion annually to poor data management. The numbers are hard to ignore and it is estimated that all combined data will be worth $229,4 billion by 2025.  Fundamental changes More important than the financial value of this data is how this data is transforming our lives at a fundamental level. From speech recognition to self-driving cars, our dependence on data will continue to grow. Every time data helps us choose the best route to work or what movie to watch we leave a trail of user data. This data is incredibly valuable for corporate decisions – if the data is used properly. Data at this scale brings huge benefits but equally huge risks.  Data is growing rapidly, daily. The quantity of data can easily become overwhelming. Businesses can therefore no longer approach data passively. Businesses that don’t actively monitor and control incoming data can quickly lose oversight. It is therefore crucial for these businesses to gather and store this data in a coherent manner. However, even though we live in a data-driven world, this remains a large challenge for many businesses. There are perhaps no industries where this challenge is more tangible than the financial services industry.   Whether it be getting a mortgage or acquiring capital to start a business, we’ve all had to deal with the cycle of getting loans approved. Financial institutions gather a lot of sensitive data on us in this process, including IDs, salary statements and information on collateral. Next to this data, financial institutions also gather less sensitive data about users continuously. This data is generated when online purchases are made, cash is withdrawn, card payments are made or someone sends a smartphone payment request after a night out.   Effective data management The problems that come with managing this data are immediately apparent. Most financial institutions are working with an organically grown IT-landscape consisting of legacy systems that don’t integrate well with one another. This silo-shaped structure leads to an incoherent form of data collection and storage which, in turn, leads to what’s known as “outlier data”. Outlier data is data that is incomplete, incorrect or stored in the wrong place. Once this data separates from the rest the risks become clear. For example, imagine that client A asks to see the data a financial institution has gathered on him or her, which is a common occurrence these days because of the GDPR. If data from client B is erroneously stored in this set of data, the institution is then dealing with what’s known as a data leak. Such leaks lead not only to catastrophic reputation damage but can also lead to astronomical fines and increased scrutiny from regulatory authorities.   Averting risks With these risks in mind it should come as no surprise that financial institutions often have entire departments dedicated to mitigating these risks. Additionally, the role of CPO, or Chief Privacy Officer, is becoming increasingly important in the Netherlands. Aegon and ING are two examples of organizations where data protection is one of the highest priorities. This makes sense when one considers the “uphill battle” such financial institutions are dealing with. In 2019, financial institutions were fined $36 billion for facilitating an estimated $2,1 trillion circulating in financial crime. On average, financial institutions spend $48 million on combating financial crime yearly but the contrast with what’s available to criminals is stark. For every euro invested in combating financial crime, two euros remains available for financial crime itself.   It has become clear that reactive approaches from financial institutions are not sufficient. Many organizations only conduct proper customer due diligence once the damage is done, they’re being audited or a fine has been issued. This reactive approach has given way to an entirely new market segment known as customer due diligence remediation. Essentially, this form of remediation entails solving an information backlog that should have been dealt with earlier.   Insight into historical customer data A recent Hyarchis report shows that only 15% of financial institutions have complete insight into their historic customer data. This is a shocking statistic when one considers the sensitive nature of such data. As mentioned, this data doesn’t just encompass customer identity but also things like income, medical data and marital status. In 85% of cases, there is a lack of coherent insight into this customer data, while these files are supplemented daily with a large amount of other sensitive customer data.  This problem often occurs because there is more data coming in than can be efficiently processed. In other words, the problem gets bigger with every day that passes. This phenomenon is causing a paradigm shift in which CPOs (Chief Privacy Officers) become crucial to organizations. Compliance as a “must” thanks to legislation and regulations is giving way to a more proactive approach. In this approach, the emphasis is not so much on averting risks as it is on creating added value from static customer data. Initiatives that were previously used reactively with the aim of knowing your customer increasingly lead to the insight made possible by holistic customer views, such as better customer service or a more targeted form of upselling.  Artificial intelligence From the recent Hyarchis research report we see that half of financial institutions are using artificial intelligence to get a proper grip on their customer data. This rapidly evolving technology is helping to reform the traditional silo-shaped IT-landscape and to structure chaotic data into coherent insight into customers. In the financial services industry, this is leading to a bona fide AI race. At present 35% of financial institutions use artificial intelligence at a functional level – this is predicted to grow to 95% in the next three years.   Artificial intelligence has thus gone from a technology that was “nice to keep in mind” to a “mission critical competency”. Artificial intelligence is increasingly becoming the answer to the overwhelming quantity of data currently being generated. Not only is artificial intelligence helping ensure that customer data is managed properly but it is also helping organizations gain proactive control of historical customer data. This helps eliminate the need for auditor visits and increasingly stringent supervision but also unlocks the full value of customer data. This approach allows financial institutions to mitigate risks while actively discovering financial advantages.   Click for the infographic

The need for fast and effective Customer Due Diligence (CDD) is accelerating up the boardroom agenda at banking, mortgage, and insurance providers. Fortunately, some institutions are finding easier ways to achieve Know Your Customer (KYC) compliance — freeing themselves from spiraling costs and complexity. Just weeks ago, an NTT Data report included a startling fact. The top 10 percent of the world’s financial institutions spend at least US $100 million on CDD each year. It wasn't a complete shock to industry watchers. Previously, Celent had estimated global spending on Anti-Money Laundering (AML) and KYC compliance technology, operations, and data was on course to reach US $32.1 billion in a year. These might seem like monumental sums to justify — unless you're aware of the penalties imposed relating to AML and KYC. These weigh in at a staggering $26 billion, according to estimates in the NTT Data report. But the true cost goes way beyond the fines. As compliance failures hit the headlines, the reputations of financial brands suffer severe damage. Customer confidence and revenues slump. Sense of urgency The growing anxiety around KYC has created a sense of urgency over the adoption of CDD technology. A separate report from Thomson Reuters found that 60 percent of C-suite respondents said they had dedicated more time and attention to KYC challenges. In fact, large financial institutions (with a $10 billion-plus turnover) have increased expenditure so sharply that nearly one-third of their annual global onboard spend is now on systems to ensure regulatory compliance. Organizations need KYC and CDD expertise to guard themselves against fraud, money laundering,  terrorist activity, and other risks. Legally, they must be in control of data at all times. But they face three specific challenges: Challenge #1: Knowing the risks you face Financial services providers must be aware of the fine detail of legislation and exactly where they're vulnerable, whether it's from GDPR, the latest European Anti-Money Laundering Directive, or another legal requirement. They must also understand how these relate to the customer data they hold, including IDs and passports, BSN numbers, medical data, names, nationalities, and more. Challenge #2: Conquering your archives A decade ago, some data might have seemed irrelevant, but now it's of critical importance. For example, personal information might be present within paperwork from a doctor, or a fax from an intermediary from 2008 and no-one realizes this — but today, this is sensitive data. The task can feel overwhelming when institutions struggle with archives of 300-400 million pages going back 20-30 years. Some investments may have transferred hands too, so there's even more data that's not indexed correctly or understood. Challenge #3: Keeping everything up to date Historically, OCR has helped, but it isn't a solution. Data can be fragmented, inconsistent, and unstructured. And it often stays that way. The Thomson Reuters report found that only one-quarter of financial institutions schedule periodic checks and just 11% use dynamic checks to ensure records are always up to date. But organizations need digital transformation in this area to take control — and stay in control. Harnessing innovation In an increasingly regulated world, forward-thinking financial institutions must know their data intimately and recognize quickly where any customer cases require additional security and control procedures. For example, effective CDD should identify people with a heightened risk profile or customer files in which critical data is missing or hidden — so a financial institution can take action. For this to happen, organizations must adopt an innovative approach and use proven technology to streamline KYC processes and stay ahead of the regulatory curve. This requires taking control of your data across archives of all types, using artificial intelligence to manage potential risks effectively — at speed and scale. With the right platform, KYC and CDD can be embedded within organizations as automated processes — so every fragment of data is understood, it's in the right place, and can be found in moments. Flexibility is vital in an ever-changing business environment. After all, you never know what the regulators in 1-5 years' time will claim that you should have been indexing today. Discover more Take a closer look at our CDD factsheet which explains more about how it's possible to extract relevant data from unstructured source documents, so you can carry out risk profiles and protect your organization. Also read about tech advances within governance and compliance.

Financial services companies are discovering that artificial intelligence can do more than save them time — it can enhance the customer experience. Using data extraction to automate routine tasks isn't the end game, it's just the beginning.  Across the financial sector, AI is getting to work. It's saving companies a fortune by automating routine tasks as well as strengthening their data governance and ingeniously hiding and retrieving sensitive ID information buried within large archives. But another area with vast potential is data extraction, a process where AI uses optical character recognition (OCR) to identify specific data points within layers of text. Put simply, algorithms spot patterns in documents and collect key information that might have taken hours to locate manually. The possibilities are endless. Virtually any business document follows some kind of format and has standard data points. It could be an identity record, appraisal report, or a sales contract. Where important data points exist, AI will find them. In financial services, around 90% of standard work processes can be automated with data extraction playing its part. Staff only need to handle the 10 percent of exceptions that require some human help. In a recent project, our customer faced the challenge of having to identify crucial customer data scattered over 25.000 customer files and 2 million documents. With the aid of artificial intelligence, we extracted this information with an accuracy of 95%.  Hence freeing up our customer’s employees for value-added tasks, such as focusing on customer care. That's a huge win in itself. But there's far more you can do with data extraction. In fact, it presents a major opportunity to enrich your product offering. Making interactions personal Data extraction has a second application: It can populate your other company systems automatically and then be used to transform the customer experience. Right now, we're working with a financial services client on a project where we extract data from their DMS archive and then import it directly into their ERP system. The ability to extract an individual's identity data automatically from documents — such as first names and birth dates — means the company's customer service can now be more relevant and personal. Extracted data can also support each customer touchpoint. As an example, let's take a request for payment from a kitchen supplier. Financial services companies can use AI to extract key data from the invoice, locating and validating the firm's VAT details and business registration number. As a result, the customer can safely transfer funds from their account. Fraud checks happen before the transaction, not afterwards. These are just a few examples. But the potential is immense for financial services companies. Once the extracted data is in your CRM or another core system, you can use AI to engage better with customers throughout their journeys, based on context, timing, and a host of other factors. Discover more If you want to accelerate your business processes, save hours of staff time, and improve the customer experience — all at once — then data extraction is the way to go. Get in touch today and our experienced team will be delighted to explore the possibilities with you. There's far more to artificial intelligence than meets the eye.

Do you want to focus more on driving innovation — and spend less time trying to prevent data compliance breaches and fines? Now, breakthrough tech can prove a game-changer for financial services companies across the UK, Germany, and the rest of Europe. Whatever the post-COVID-19 world looks like, managing sensitive data will be part of any 'new normal' for financial services companies. But it's a task that's getting tougher. Every week, it can seem like new regulations are arriving from local, European or global regulators. Companies are grappling with complexity — from the far-reaching ramifications of GDPR, to Bank Secrecy Act/Anti-Money Laundering requirements from the US. Across the world, there are swift moves to target money laundering, tackle the financing of terrorism, and impose sanctions on oligarchs and their network of companies. The pressure is ramping up on financial services companies. The risk of compliance breaches, fines, and damage to brands has never been so severe. But, at the same time, companies are having to comb through an expanding quagmire of unstructured data across multiple systems and silos. So how can you free yourself from this draining scenario — and focus instead on finding fresh ways to sharpen your competitive edge? Using AI to strengthen governance It's an uncomfortable truth, but many archives today have grown so large and complex that it's now impossible for humans to monitor compliance and get any sense of 'who their customers are'. But artificial intelligence can be used to fully automate the process, even if you're grappling with millions of documents. For example, what happens when personal ID information is needed for transactions and processes — but the same data cannot be text-searchable? While optical character recognition (OCR) is nothing new, the latest AI engines have improved accuracy and speed remarkably through machine learning. New tech can blur ID data automatically while leaving the integrity of the archive intact. It's proving an invaluable way to strike that perfect balance between privacy and verification. Quality control is a great place to start. New AI tools for data integrity will let you dive into any new, historical, and unstructured customer documents — across different repositories, so you can get a unified approach that protects your entire business. Very quickly, you can scan huge amounts of data for completeness, remove information that shouldn't be there, and check documents are stored in the correct customer file. As a result, it's far simpler to ensure the data complies with legislation. Next comes customer due diligence. Here, AI saves hours of human effort in an extremely sensitive area. You can analyze vast volumes of data, start to know the customer better, and spot files where critical data is missing. By recognizing unusual behavior, the AI can flag up heightened risk profiles and potentially suspicious transactions. Finance companies can be the first to know — not the last. Discover more As countries emerge from the COVID-19 pandemic, financial companies need to be more resilient, positive and innovative. So, rather than viewing data compliance as a necessary evil, see it as the door to something greater. With the right tools and techniques, you can adapt to regulatory change faster. But you can also accelerate your digital transformation process and gain an advantage in your marketplace — by becoming a more insightful and agile organization. If you want to focus more on driving innovation, then get in touch. Our experienced team will be delighted to explore your data governance challenges and find smart ways to strengthen their privacy.

“Some organisations use Artificial Intelligence (AI) to blur personal information within their archives. Others use AI to retrieve ID information from scanned passports and ID cards, in order to enrich their databases.” This month in AI on the road, Krijn talks about the dual use of AI in processing passports and ID cards. In the column “AI on the road”, Hyarchis Artificial Intelligence expert Krijn talks about the AI-related questions that he encounters during meetings with clients. In other words, very basic questions and answers about this seemingly complex topic. This month Krijn focuses on the use of AI for managing sensitive passport- and ID-card-related data within large archives. Krijn, let’s start with using AI to remove ID information. What questions do you encounter?The questions that arise around removing ID information, or better said: making that information inaccessible, are often based on compliance with GDPR legislation. Various organisations are burdened with large archives containing millions of copies of passports and ID cards. Under the current regulation, it is important that access to sensitive data, such as personal identification numbers (PINs), is restricted. AI offers the possibility to do this while leaving the integrity of the archive intact. The Dutch website InFinance recently published an article on the subject, in which they outlined how to correctly process PINs in line with the regulations of the Dutch Data Protection Authority (Dutch: Autoriteit Persoonsgegevens). AI gives organisations the ability to track down personal identification numbers in documents which are not text-searchable. As a result, organisations can find and subsequently hide sensitive data in an automated manner even in outdated or complex archives. Could you give an example of information that organisations need to blur?Before GDPR came in effect, the PIN of someone who was applying for example a mortgage was recorded and saved in this file. Under current legislation, PINs are not to be disclosed in a this application, unless this is essential to the purpose for which the data is processed. Only when the contract is actually signed, the personal identification number can be recorded and included in the case file. Because of this, many organisations are now wondering “How do I make this number inaccessible?” What role does AI play in all of this?It is impossible to manually remove specific information from scanned passports and ID cards. Passports might be scanned horizontally, vertically, crooked, or two scans could be even combined on one page. Add the wide variety of layouts – every country has their own passport design – and you start to understand the magnitude of this challenge. Step one is automation. However, what do you do if not only the search process has many variables, but the blurring process too? That is where AI comes into play. Using OCR-based technology, our software solutions recognise personal identification numbers within scanned documents with a near 100 percent accuracy. Once found, AI makes sure that these numbers are blurred, making them unrecognisable. What is the difficulty that you address by using AI?It is often unknown where the files containing passports and ID cards are located. Most of the time the files are not named accordingly and located somewhere in a 50-page personal file. AI traces these regardless of the file type or file name and ensures that the PINs are blurred and no longer accessible. If I understood correctly, AI is not only used to remove information from digital files, but also to add information to the database? Tell us more…Indeed, clients also use AI to extend their database. In the past, passports and ID cards were not always registered properly, because there was no need to do so. Nowadays, organisations do need to implement appropriate data minimization measures and store personal information transparently for the customers in order to serve them better and comply with data privacy laws and regulations. Now I’m curious…is that possible?Yes, it is possible, but to do that, you need to use AI. If an organisation would like to recognise the data on an ID card or passport that is in their archive, our AI solutions can help with that. Whether you want to remove existing information from your archive or do the exact opposite, our AI-based software works both ways! Learn more about our Business Intelligence and Governance solutions.