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Eliminating false positives for banks in their AML drives

Eliminating false positives for banks in their AML drives

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.

https://www.dqindia.com/eliminating-false-positives-banks-aml-drives/

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Advanced Analytics in Banking World

Advanced Analytics in Banking World

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: https://www.finextra.com/blogposting/17951/advanced-analytics-in-banking-world

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Sentiment Analysis in banking

Sentiment Analysis in banking

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.

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Open Banking & Importance of Analytics

Open Banking & Importance of Analytics

Open Banking has arrived

The banking industry is on the cusp of a revolution, driven by open banking parameters and new regulations for entry of third-party providers. Introducing open banking could mean enhancing customer banking experiences, reducing overall costs, diverting investments towards technological advancements and more. This induces fragmentation of existing clustered banking markets, enabling accelerated innovation and development. While Open banking is an ecosystem comprising of banks, third party API developers and regulators that makes ‘customer experience’ more holistic and rewarding, it can be effective though only in conjunction with near-real-time data analytics. Analytics is essential to improve risk and compliance in near-real-time by all players on the open banking canvas.

According to a report by World Retail Banking, more than 78% of banks wish to leverage APIs to improve the customer experience through open banking. These APIs should be scalable and have the ability to track and measure monetization.

Analytics is Key

Open banking will relinquish the control that big financial firms had over the current market using open APIs which makes it easier for third parties to access customer data and create services by plugging in directly to bank systems. These trends and developments in open banking will reshape the industry and drive the adoption of open APIs in the years to come.

It is no longer the data you hold but what you do with it that will deliver a competitive edge and an entrenched role in the customer’s life.

The open banking movement has entered a climate where customer relations is fragile since today, people crave connected experiences and are forever lost as customers when brands fail to recognize them as individuals. Having easy access to a customer’s entire portfolio helps devise new products and bolsters customer experience and innovation. This aids the bank to usher in different methods of customer engagement like a chatbot application for instance that personalizes the customer experience. Redesigning a bank’s customer acquisition model can help create agile machine learning models that can adapt with customers when they are inactive.

PWC estimates that by 2022 open banking will open an opportunity of 7.2 billion pounds. Powered by data analytics, the open banking market is set to take banking to the next stage of improved revenue and better customer experience.

Some Predictions for Open Banking in 2019

  1. Increased Demand for Screen-Scraping: The Regulatory Technical Standards (RTS) of Open Banking have made it difficult for fintech companies to meet the standards due to a shortage of live open APIs. For providers wishing to embrace the open banking system, using account aggregators that furnish solutions based on “screen-scraping” is a possible alternative.
  2. Rise in Account Aggregator Supply: The list of account aggregators in open banking continues to increase. Furthermore, credit bureaus are further pushing for the adoption of account aggregation.
  3. Cost of Accessing Data Will Fall: The vast increase in account data will push for more AISP (account-specific information service provider) licenses and aggregators will bring the cost of account data lower globally. Hence, this would further encourage banks to adopt open banking and invest more in building better customer experiences.

A disruptive threat to Incumbent Banks?

With the arrival of Open Banking, customers will be willing-to-change and willing-to-experiment with new ways to manage their money, borrow, and protect their wealth. While Open banking is good news for consumers, incumbent banks have a risk of being left behind by their customers and hence must act quickly to stay relevant. The winners will be only those who will be data-driven and leverage Analytics to rapidly convert customer insight into powerful new experiences that are personal and add real value to daily life.

Despite the threat of extreme competition, banks do enjoy a clear head start over challengers. In reality, it’s an opportunity for banks to evolve digitally. Being an incumbent, they have at least one clear advantage i.e. access to a sizeable pool of existing customers. By keenly paying attention to their customers, they can gain insights into the products and experiences they want most, now and in the future. While open banking regulations work in favor of the customer, they also ensure that ample data is amassed for the benefit of the banks themselves. From behavioral profiling to strategizing, analytics helps in creating better business plans. Using patented analytics tools, banks can understand a customer’s typical purchasing pattern. From spending velocity to the exact hour of transactions and how customers spend money in relevant risk, pricing and cross-selling models, the algorithms help track the exact purchasing behavior of the customer. By using machine learning algorithms, customer profiles can be created, and their hourly & daily transactional behavior tracked thereby preventing anomalies.

Understanding the patterns help devise methods of improving customer experience, while also maximizing the bank’s profitability margin. This improves customer trust and brand loyalty and increases the chances of long-term customer relationships and profitability. This part of analytics can be personalized based on financial and non-financial parameters, helping clarify customer profiles further.

Real-time Analytics to fight fraud

Even though Open Banking is clearly the way forward, there are certain challenges that accompany this new wave. The added conveniences provided to consumer banking will lead to rising in transaction volume which in turn is likely to make banks more vulnerable to fraudulent and illicit activity. But if you catch fraud in real-time, you have the opportunity to stop it before any big damage occurs. It will take human beings too long to parse and interpret information that reveals fraud, thus Real-Time Analytics-Based Detection is Key to catch fraud. Banks can establish a baseline of normal transaction activity and there on identify anomalies using real-time analytics that could signal potential fraud.

Start today

The harsh fact some of the popular surveys have revealed is that Less than 20% of surveyed banks make extensive use of predictive data analytics to power improvements in the customer experience and operational performance Analytics can help banks become smarter in tackling challenges and analytics services in banking is still in its infancy with a lot of untapped value still left to be uncovered. The overall surge of analytics in banking is increasing and is expected to quadruple by 2020.

Analytic solutions today can predict a bank’s profitability, ensure survival and compliance and further foster growth. Instilling a growth-driven mindset to leverage analytics can greatly improve decision making while also taking other ground realities and challenges into consideration. Banks have a lot of ground to cover in the next five years if they are to retain the customer relationship in the era of open banking.

This “start today” approach is recommended.

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Data Democratization is key for Digitalisation

Data Democratization is key for Digitalisation

Data is a valuable currency to organizations that are looking to accelerate their business, gain a competitive edge, and build value adds products for their customers. But with the rise in smartphones and ease of accessibility to the internet, a large volume of data is generated that is scattered and unstructured. In today’s data-driven world, meaningful data insights play a crucial role in decision making. In the face of Digitalisation (also, Digital transformation), data collaboration is of utmost importance.

Right now most organizations have centralized back-end analytics teams that run the data analysis once in a while and identifies actionable outcomes based on its own judgement. Instead, imagine a world wherein these backend analytics teams become enablers rather than controllers and the end-user directly access analytics on a real-time basis. Data democratization can provide the much-needed barrier-free access to data.

Data democratization is in fact the first step towards Digitalisation. It mandates that data be accessible and understandable to end-users (internal and external) to be able to make data-driven decisions. The goal is to avail data at any time, with no barriers/gatekeepers to access or understand data. Traditionally, the banking sector has operated under data silos. Data is owned by disparate business units, divided by distinct lines of applications, geography, data stores, and hierarchy. With data democratization, customizable analytical tools are deployed to desegregate and connect siloed data. The benefits of data democratization, among many, is in delivering better operational efficiency, business intelligence (BI), fraud detection, financial reporting, discovering customer profiles, record keeping, and customer experience.

Democratization of data across an organization opens up new opportunities that simply wouldn’t be possible with traditional BI tools. For instance, the Royal Bank of Scotland moved to data democratization to sort their digital marketing initiatives. Data existed in siloes of call centers, human resources, and legal department. The IT teams realized the need for data democratization and providing non-marketers access to unified data. By analyzing and understanding the data, non-marketers were able to provide new insights into the marketing process and optimize the customer’s experience.

More and more banks are slowly realizing the need for a collaborative approach toward data and its availability to all users across the organization. Distributing information across teams and business units would empower individuals at all levels of hierarchy and responsibility to use the data for better decision making.

But of course, while data democratization is considered to be the latest industry game-changing buzz word by many, there are always common excuses and some genuine fear about data Integrity and data Anarchy that are used to avoid Data Democratization.

  1. In a highly regulated Banking industry, data security and compliance are the priority. This user-centric architecture with open access to data is a security concern for organizations.
  2. Traditionally, data analysis and insights are governed by data scientists or analysts. Data democratization raises the issue of data misinterpretation by non-skilled professionals.
  3. There are technological and organizational barriers to realizing the full potential of open data in the banking sector, including the inability of banking systems to provide standardized information.
  4. Data sources can vary and providing open access to several users brings up the issues of data ownership, often pitting users against technology.
  5. To drive better insights and compliance requires replacing legacy systems with modern platforms and expansive data storage facilities — an expensive affair for smaller financial institutions.

Despite the concerns, data democratization is an essential part of digitally-aware organizations. Strict governance over the collection, interpretation, and dissemination of information would help address accountability and form a center of excellence.  There are several ways an organization can guarantee success in a Data Democratization initiative and provide the Right-data-for-each-person. The biggest driver for data democratization is the technological advancements in data storage, analytics, and visualization tools.

  • Data virtualization and federation: When dealing with sensitive information, companies have to take care of protecting user information for security and compliance. Data virtualization and federation tools are catering to the bank’s need for data anonymity. It creates an abstraction layer that addresses issues of access, real-time integration, duplication efforts, and hides all the technical aspects of stored data (location, API, storage structure, storage technology, etc.).
  • Cloud storage: While big data is helping develop better analytics to manage vast and disparate data sets. The landscape of this ecosystem is supported by easy cloud storage solutions. Governed data lakes are capable of storing raw and processed data aggregated from several sources (emails, messages, social media, text, etc.). Cloud storage is enabling banks to break data silos by centralizing data stores.
  • Self-service: Static reports are dead and there is a need to move towards self-service interactive reports. Enabling the modernization of banking analytics requires the replacement of legacy systems with platforms that enable self-serve analytics. By deploying self-service interfaces, banks are able to accelerate data quality and governance.
  • Data monetization: As banks continue their search for new revenue streams, monetizing enterprise data opens up banking-as-a-service platforms. For instance, in the EU, the open banking mandate set a precedent for data sharing banking. The revised payment service directive (PSD2) enables the democratization of customer’s data and payments. Banks share their customer’s data with third parties through public/private application programming interfaces (APIs). This democratization of data has enabled banks to serve as a platform for providing easy access to data, tools for correlating and contextualizing data, and the real-time data analysis quicker decisions.

So, are we there yet?

Above are just a few of the considerations to ensure appropriate Data Democratization and Data Governance. The challenge in data democratization still lies in sharing the data within the stringent compliance rules of the banking sector. Achieving data democratization is not a singular goal, rather it is an ongoing process. Organizations armed with the right tools will be able to navigate the digital landscape. To succeed, companies have to completely govern and manage the usability, availability, integrity, security of the data, irrespective of its location.

Data Democratization is the new normal. Finding a trusted partner would help build the ecosystem required for companies to function to its full potential.

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