Just like the famous Gold Rush of 1849, nowadays businesses are dipping their toe in the data mine, in order to seek some value out of it. This huge chunk of data is forcing the fintech and the banking industry to unleash the power of the hidden gems that data analytics can deliver.

One can’t deny the fact that banks and financial institutions generate astronomical amounts of data in the form of customer transactional and non-transactional data. Reports state that 2.5 quintillion bytes of data are being generated every day. Have we ever thought whether this data is a promise or a peril? It is no surprise that conventional data-processing fails in managing this large volume of data and provides insights that are far from reality.

Realising the value of big data requires an analytical eye and technologies such as big data analytics, AI, and machine learning. These help in churning down data into meaningful information, thus minimising the risk decisions based on intuition.

That’s where the role of data scientist comes into the picture. A data scientist has mastered this treasure hunt as it requires them to know exactly what information to look for that will act as a booster in cross-selling and customer satisfaction. In the banking industry, a data scientist can help develop customer profiles, predict behaviours and track trends, to name a few.

According to a survey, the banking and financial services sector is the biggest market for analytics and data science professionals with 44 per cent of all jobs created in this domain. This percentage will grow in the coming years as this sector is actively using data to derive business insights and improve scalability.

The emerging role of data scientists

Over the past few years, the banking industry has achieved new heights through innovative means for evolving customer expectations of personalisation and convenience.

Earlier banks and other financial institutions used to follow a one-size-fits-all strategy where every customer was treated with the same approach irrespective of their needs and interest.

Gone are those days when customers would visit banks for every single service like depositing, checking account balance, etc. Customers now use their mobile phones to check their account balances, deposit checks, pay bills, and transfer money.

According to a research commissioned by Relay42, the data management platform (DMP), “Digital banking is growing in popularity with 53 per cent of consumers using or willing to move to an online or mobile only bank — 27 per cent have moved already, while 26 per cent are considering the switch”.

There was a time when it would take a few years to build a framework that helps banks in gathering an overall picture of their customers. Since online banking is gaining popularity, adopting big data analytics becomes all the more important. Thus, all this has given room to the new and ever-growing career of the data scientist. A data scientist helps provide meaning to the raw data and uses it to draw insights for better analysis. They help banks in establishing a 360-degree approach for their customers by the analysis of:

• Customer spending patterns
• Customer segmentation
• Implement risk management processes
• Customised product offerings
• Customer loyalty

In addition to this, data scientists help banks in designing, building and maintaining the complex data flows, tools and solutions that are needed from the bank’s data systems to analytics environments.

Moreover, data scientists tend to toil away in advanced analysis tools such as R, SPSS, Hadoop, and advanced statistical modeling. Thus, easing the process of generating valuable information from the piles of data and provide inside based on key metrics with suggestive best practices.

It can be rightly said that the fintech domain has benefited from the emergence of analytics.

The path forward

It’s high time that banks adopt big data analytics to remain relevant and profitable in this hyper-competitive business environment. Experts like data scientists will be an edge to these growing trends and will bolster the future of the fintech industry.

This career has never-ending benefits and it poses a promising future for the data science space as data has become the new oil to drive decision-making. One of the biggest challenges faced by the modern banking industry is legacy systems that aren’t equipped to handle the big data revolution. So, banks will need to align their people, processes, and technology platforms to provide highly personalised customer experience by extracting insights from data.

This article has been published in Express Computer

Read now: How data scientists can bolster the future of fintech industry

One of the biggest regulatory and compliance challenges financial institutions face today is the high rates of false positives generated from their AML (anti-money laundering) monitoring systems. Despite designed to identify suspicious transactions involving illicit proceeds for illegal purposes, traditional TMS (transactions monitoring systems) are fundamentally antiquated.

They lack the ability to assess the context of a transaction by a customer, the agility to react to rapidly evolving digital patterns used by money launderers and the ability to understand clearly, why a transaction can be possibly suspicious. Over the last few years only, global cases of money laundering have amplified manifold, leading enforcement agencies to update the regulations in respective countries. The EU’s latest “blacklist” due to the $221 billion money-laundering scandal involving Denmark’s Danske Bank is one such example . (Quartz)

Closer home, banks have spent ~$321 billion in fines since 2008 in lieu of regulatory failings, terrorist financing, money laundering, and market manipulation. In fact, as per UNODC, ~2 to 5% of the GDP worldwide is laundered globally each year. (Business Standard)

In an environment of rising regulatory demands and spiking screening volumes, artificial intelligence (AI) and machine learning (ML) can be the only viable option to accurately detect suspicious transactions. There are a few challenges, however.

False positives: The double-edged sword for financial institutions

Almost all banks and financial institutions are implementing advanced verification systems, adding stricter criterions to accept new customers and increasing PEP (Politically Exposed People) screenings. The scrutiny of customer public records has gained so much momentum that banks include negative publicity/news as an assessment factor. This becomes a double-edged sword for banks and financial institutions. Because as banks inspect more deeply, they also need to tread the thin line of customer privacy violations. The more questions banks ask, the more uncomfortable the customers become.

Of course, AI has its benefits in battling financial crimes in banks, i.e. it can improve the effectiveness & efficiency of investigations and improve their risk management practices. There is however the common element of “false positives”, where banking systems end up flagging a legal transaction as discredited. Inaccurate data in an environment of data overload, therefore, is a growing concern. Consistently ineffective legacy systems have resulted in astronomical budgets, causing dropped stock values, leading to fading consumer trust and long drawn out resolutions.

However, as PWC pointed out recently in a research document, AI maturity is a hindrance for banks and financial institutions. It points out despite being aware of AI as a cheaper, faster and smarter option to tackle financial crime, there is a lot of confusion around how to harness it. The olden ways of rule-based filtering technologies are now inadequate, and inflexible to support real-time interventions, as they depend on the expert judgement from human beings. Until the recent past, banks have reasonably not taken complete advantage of AI solutions due to the concerns of transparency with “black box” models. Of course, poor data leads to poor outcome, but to avoid adopting AI unless the data is less than perfect, also removes your competitive edge.

In the age of implementation however, with Machine Learning, banks can now create a holistic viewing panel of their customers’ form static KYC documents and transactional dynamic data in a completely compliant way. Even more, an ML engine can be used on top of an existing infrastructure to run independently without troubling current operations.

Getting realistic about ML deployment in AML

Today banks do not have to shred their existing system to replace new advanced technologies to enhance legacy ones. Many banks and financial institutions have made strategic investment in data science companies that specialize in real-time fraud detection. There is also a general acceptance of machine learning and artificial intelligence in AML regulations and fraud detection.

With money laundering, cyber-attacks and breaches becoming a global menace, banks need to be sure of their AML and compliance budgets before jumping in. While ML and AI are known for being great at fully automated systems, banks need rather to start looking at them as a human augmentation tool. Soon AI and ML will possibly become critical and a core component for compliance-conscious financial institutions worldwide.

This article has been published in DATAQUEST.

Read now: Eliminating false positives for banks in their AML drives

Through the ages, data analytics has been a key aspect of every financial institution. From invest banking to credit scoring to securities trading – data analytics has played a major role in arriving at a data-driven decision. With the advent of technology, big data analytics has gained significant ground in the banking and finance sector. In the last decade, the explosion of big data has opened up enormous potential for banks to grow and stay relevant. While basic data analytics is a critical component of banking strategies, the use advanced and predictive data analytics is growing to help provide deeper insights.

In order to adopt advanced analytics, banks have to understand the components that make up the technology. The main components of advanced analytics can be broken down to four categories:

• Reporting: focuses on conversion of raw data into information, building data repositories using basic analytics. For example, reporting suspicious activity.
• Descriptive analytics: processing, identifying patterns, and summarizing the information gathered in reporting. For example, customer segmentation based on spending behavior
• Predictive analytics: using the above patterns to predict future actions or scenarios. For example, personalization of customer offerings based on customer segmentation
• Prescriptive analytics: gathering results from descriptive and predictive analytics to determine what, why and how a situation is likely to occur. For example, decision optimization based on economic and consumer trends

Together, these components drive advanced analytics that enables business users to search, conduct, and analyze forecasts and predictions. For financial executives, timely and precise data is critical to arriving at business decisions. Banks worldwide are recognizing the importance of analytics and increasing their advanced analytics investments. Advanced analytics solutions are helping banks vastly improve decision making. Applications range from optimizing everyday activities to enhancing productivity. But the main application of advanced analytics has been in improving customer experience. With digital transformation overtaking the banking sector, customer-centricity is of utmost importance to banks. Banks having a growing need to assess customer behavior to understand their wants and needs, engage customers to improve customer loyalty and retention, and deliver exceptional service to improve customer satisfaction. Other benefits of advanced analytics include:

• Fraud prevention

Fraud detection is a critical activity in banking. Usually, fraudulent activity is detected by using transaction monitoring systems that require manual intervention and are time-consuming. With advanced analytics, banks are able to predict customer behavior and identify suspicious spending patterns. The alerts are sent out in real-time, impeding further fraudulent activity with quick actions (freezing the account, alerting the customer). Predictive analytics and machine learning can further be deployed to secure and safeguard accounts against repeated cyber-attacks. For example, Danske Bank deployed an artificial intelligence-driven platform to identify and tackle fraud. The system analyzes data and scores online transaction in real-time to provide actionable insights for fraudulent activity. The system has reduced the number of false-positives and the cost of fraud investigations.

• Identify & acquire customers

Banks are adopting advanced analytics to help obtain more customers through target optimization. Analytics help develop deeper customer segmentation and profiles for  the marketing team to identify the right targets on the right channel. For example, Citi Bank leverages big data analytics for customer retention and acquisition. Using machine learning, Citi analyzes consumer data and target promotional spending.

• Customer retention

In order to maximize the lifetime value of a customer, banks have to work on their customer retention strategies. Customer retention requires paying attention to the quality of service (QoS), identifying at-risk customers, and providing attractive retention offers. With the help of advanced analytics, banks can delve deeper into customer service, identify behavior patterns and paths, and use insights and conversion results to arrive at a ‘churn score’ to take preventive measure for customer retention. For example, American Express relies on sophisticated predictive models to forecast and prevent customer churn. By analyzing past transactions, the system identifies accounts that are most likely to close and take preventive actions.

• Cross-selling/Up-selling

In a revenue draining atmosphere, predictive analytics is helping banks open effective revenue streams by cross-selling or up-selling of financial products and services. With predictive analytics, banks can understand customers on a granular level, their usage and spending behavior, digital media sentiments. Powered by this information banks can now create a hyper-personalized sale strategy. For example, First Tennessee Bank leveraged predictive analytics solutions to optimize its market strategy. The highly-targeted campaigns helped increase customer response rate by 3.1 percent and cutting marketing costs by nearly 20 percent. Their targeted offers within high-value customer segments resulted in a 600 percent return on investment.

Banks have to evolve and understand the rapid changed in data analytics technologies. Adopting advanced analytics and inculcating it into the existing banking environment is one of the key elements of surviving in this digital era. The future of banking revolves around leveraging data and advanced analytics towards enhancing the accuracy of predictive models.

This article was originally published on Finextra

Read now: Advanced analytics in banking world

In today’s digital world, data has transformed to be the new currency. With the advent of technology, the volume of data generated by financial institutions has grown leaps and bounds. In this climate, banks and other financial institutions are being forced to adapt to a data-driven organization. Big data is making inroads into providing critical information for marketing & sales, operations, business performance, and risk management.

But being a data-driven organization brings forth the challenges of managing big data and its compound characteristics. While the 3 V’s of big data, namely volume, variety, velocity define big data, the most important aspect of it comes down to the value it delivers – the fourth V.

Big data is worthless if stakeholders cannot derive value from it. This is where big data visualizations give form to this unstructured data, moulding it into something visual, tangible, and relatable. Visualization provides key actionable insights from complex datasets for all business users and not just data scientists. In the banking sector, data visualization projects can range from visualizing historical trends to real-time analysis of transactions to complex network analytics. With visualization, banks can now make sense of large volumes of data, making it easier to spot patterns and trends.

For instance, visualization is essential for the prevention and detection of money laundering. According to PWC, money laundering accounts for 2 to 5 percent loss of global GDP, or $800 billion to $2 trillion. Visualizing data has helped in finding patterns in unstructured transactions, track relations, identify below the threshold smurfers, and communicate results for quicker action. Visualizing this unstructured data provides deep insights for enhanced due diligence.

Today’s data visualization tools go beyond the conventional static visualization methods (tables, histograms, line charts, bar graphs etc.,) to interactive data querying and dynamic visualizations. For example, real-time dashboards can help portfolio managers to uncover risk concentrations and highlight portfolio improvement opportunities.

A good data visualization is part of delivering a data-driven story to your business users and consumers, alike. It forms the ‘front-end’ of data that relays a simple and quick snapshot of data insights. Business users can make key observations about their customers to help deliver a better customer experience. For instance, a marketing analyst could map historical data to identify spending patterns and gather insights towards financial management (and potential cross-selling opportunities such as fixed deposits, savings bonds etc.,). On the other hand, consumers are presented a visual representation of their spending in an interactive dashboard; improving customer experience and engagement.

Big data analytics has become the main driver for innovation in the banking domain. But, the very sensitive nature of the financial data brings in the challenges of scalability, dynamics, functionalities, and response time for visual analytics. As banks assimilate data across channels, the diversity and heterogeneity (structured, semi-structured, and unstructured) of data, the need for better visualization is crucial.

The first step for banks to step into data visualization would be to incorporate and adhere to the data analytics and visual practices. The key to simplify the monitoring and visualization of vast data is an intuitive dashboard that is accessible for all end-users to map the entire customer journey.

In this data-driven world, data analytics is critical for a better understanding of consumer behavior, meeting regulatory requirements, and generate new sources of revenue. Across industries, business leaders are working on embedding analytics into decision making and operational workflows. And driving these analytics are visualization engines that empower users to derive insights toward better risk management, increased profitability, and enhanced growth performance.

The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this competitive environment, banks are realizing the importance of customer service and satisfaction and want to pay close attention to the Voice of Customer to improve the customer experience. By analyzing and getting insights from customer feedback, banks will have better information to make strategic decisions. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis.

What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases.

Sentiment analysis, also called opinion minion or emotional AI, is a series of algorithms based on natural language processing (NLP), text analysis, and computational linguistics. The algorithm is designed to identify the types of comments or reviews (positive, neutral, or negative) based on the words used by the customers. NLP, often confused with text mining, is an advanced analysis technique used to filter large amounts of research and extract relevant information. NLP forms an integral part of text mining but uses a variety of techniques to understand the complexities and sentiments of human speech and natural text. Today, the use of NLP is widespread, hidden behind chatbots, virtual assistants, online translation services, and much more.

The NLP algorithm determines a customer’s sentiment using either a dictionary (Lexicon-based wherein words are annotated by polarity score), machine learning (constructing a classifier to identify specific text) or hybrid (combination of machine learning and lexicon) method. Usually, sentiment analysis tasks are modelled as a classification problem, i.e., classifying a text to a class. Here, two problems must be resolved:

• Subjectivity classification – subjective or objective classification of a sentence
• Polarity classification – a positive, negative, mixed or neutral opinion of a subject or object

For example, a sentence like “The customer service of XYZ bank is frustrating” – the system identifies “customer service” as a feature, “XYZ bank” as the object, and “frustrating” as a negative opinion. The algorithm arrives at a relationship between the opinion and object to extract relevant information.

Today, several banks study and track customer behavior through websites, transactions, voice notes, social media, and other digital channels. The aim being, to map and monitor a customer’s journey with a bank and how those paths affect the quality of service or the sale of financial products and services. Financial institutions are collecting data through polls or interviews to capture customers opinions towards specific product or service. Analyzing the unstructured data through semantic processing offers a comprehensive view of customer satisfaction; classifying it under negative, neutral and positive feedback. Using the insights, banks can deliver better customer service by:

Personalizing customer engagement

Keeping a record of customer sentiments would help guide customer service teams to engage with their customers better and deliver personalized experiences. Social media listening tools can be deployed to understand customer behaviour and interaction and arrive at a data-driven marketing strategy. For example, Amex’s Go Social program delivers insights to merchants to create social and mobile offers for their customers.

Prioritize customer issues

Customer support systems can use sentiment analysis to categorize customer support tickets or comments based on the criticality of the issue. The automatic analysis of sentiment of the text can then prioritize the issues, helping customer support teams focus their effort and time on highly critical issues first.

Improving banking products and service offerings

Social media monitoring is helping financial institutions gain a comprehensive understanding of how customers react to their offerings. For example, BBVA Compass analyzed social media comments to improve its rewards system. Through the insights, BBVA was able to identify trends, capture competitor product benefits, and understand how social media users comment on the bank. The result – BBVA raised the cash back rewards on its credit cards.

While sentiment analysis is used across industries, it comes with its own set of data challenges that can be classified under – volume, language ambiguity, and text size. The new-age, tech-savvy customer is generating a huge amount of unstructured data. Combing through this mountain of emails, texts, support tickets, chats, etc. is a difficult task that is time-consuming and expensive. Using machine learning, banks are able to reduce the computational burden of analyzing text, but with text comes the challenges of general sentiment analysis issues (irony and sarcasm, word ambiguity, negation detection, neologisms, idioms, and multi-polarity). In addition, NLP algorithms are generally written to analyze large body of texts that offer context and more information. In  the case of short texts (reviews/comments), the syntax  and context of the written language is lost and cannot always apply to traditional NLP techniques. Incorrect segmentation of text leads to incorrect semantic similarity and increasing ambiguity of data.

NLP has been successful in handling the syntax of a natural language, but the technology is far away from meeting the challenges of semantics of natural languages. Delivering exceptional customer service does not end with sentiment analysis alone. As the volume, diversity, and complexity of customer feedback data grow in the banking sector, there are further challenges to be addressed with sentiment analysis. With the evolution of technology, semantic analysis will grow to become an integral part of customer service.

Customer Experience is the new battlefield.  The harsh reality remains that a bad experiences can be shared with millions of people around the world within a matter of seconds. A good reputation takes years to build and seconds to destroy, hence managing risks with a technology that flags high risk social posts (reviews, mentions, tweets and blog) in real time is key. Listening to customers, and moving with them, can be a game-changer for the Banks.