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<ul><li>Understanding how the brain and the neural network works plays a pivotal role in the implementation of neuroscience in Artificial Intelligence (AI). </li><li>Brain neurons and AI models are both involved in processing information, however there are some key differences between the two.</li><li>The intersection of neuroscience and AI has the potential to lead to many exciting developments and breakthroughs in both fields.</li></ul>Neuroscience and Artificial Intelligence (AI) are both rapidly advancing fields that have significant potential for synergy. Neuroscientists aim to decipher the brain and its activities, and how each neuron provides commands to different parts of our bodies. It plays a vital role in the development of deep learning algorithms inspired by the mechanism that the brain uses to process information through an interconnected network of neurons. Hence, AI researchers are now focusing on building machines that can act like a human brain. <div type="image" title="1200x900" msid="106089870" hostid="261" sizebytes="78908" caption="&amp;lt;p&amp;gt;Human brain neurons and artificial neural network&amp;lt;/p&amp;gt;"></div> Brain neurons and AI models are both involved in processing information, but there are some key differences between the two, here are a few comparisons: <table style="border-width: initial; border-style: none;"></colgroup><tbody><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Factors </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Human Brain </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Artificial Intelligence </td></tr><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Structure </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Consists of complex dendrites, axons and synapses which enable communication with other neurons. </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Structured network which consists of layers of artificial neurons. </td></tr><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Learning </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Capable of synaptic plasticity which improves the strength of connections with multiple neurons. </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Capable of training based on data and adjusting the weights of their connection to improve performance. </td></tr><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Processing Power </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Can perform complex tasks with improved efficiency due to the presence of billions of neurons. </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Less efficient compared to the human brain since the data can be incomplete or incorrect. </td></tr><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Flexibility </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Existing neural circuits can perform a wide range of tasks and are adaptable. </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Cannot adapt to new and unexpected scenarios since the rules are defined to perform specific tasks. </td></tr><tr><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Energy consumption </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Energy-efficient and powerful. </td><td style="border-left:solid #000000 0.5pt;border-right:solid #000000 0.5pt;border-bottom:solid #000000 0.5pt;border-top:solid #000000 0.5pt;vertical-align:top;padding:0pt 5.4pt 0pt 5.4pt;overflow:hidden;overflow-wrap:break-word;">Vast energy consumption and less effective. </td></tr></tbody></table> <cmsannotation type="cmsannotation" annotationid="Mimic the neurons – AI POV" comment="subhead" text="Mimic the neurons – AI POV">Mimic the neurons – AI POV</cmsannotation> AI has several types of ANNs (Artificial Neural Networks) which can perform specific tasks. ANN’s performance is highly dependent on the quality and quantity of data used to train it, as well as the architecture and parameters of the network itself. These are computational methods that are replicated using schematics and actions performed by the brain. The study of a brain’s neural networks provides AI researchers with a platform to develop ANNs that can use data as input and based on patterns, provide output. And they are used for image and speech recognition, large language models, and the developing metaverse. <ol><li>Feedforward neural network (FNN): FNNs are a type of ANN that is commonly used in Machine Learning (ML). In this network, data flows in one direction, data enters the network, it is processed, and a final output is produced. Use cases are speech recognition for sentiment analytics and financial forecasting. </li><li>Recurrent neural network (RNN): RNNs are a type of ANN that is designed to process sequential data. Unlike an FNN, which processes input data in a single pass from input to output, RNNs process one element at a time. Use cases are image captioning in insurance and video analysis in fraud. </li><li>Convolutional neural networks (CNN): CNNs are a type of deep learning algorithm that is commonly used for image classification and computer vision tasks. They are designed to automatically learn and extract features from the input images, without requiring manual feature engineering. Use cases are object detection in insurance, facial recognition for KYC &amp; onboarding and self-driving cars. </li></ol><cmsannotation type="cmsannotation" annotationid="Implementation of neuroscience in AI" comment="subhead" text="Implementation of neuroscience in AI">Implementation of neuroscience in AI</cmsannotation> Understanding how the brain and the neural network work plays a vital role in the implementation of neuroscience in AI. For example, the decision-making activity in the human brain takes place in the frontal cortex of the brain. The RNN model mimics decision-making in AI. Study the neural basis of AI models: Studying the human brain including its structure, functions, and also the neural network can be useful in AI models. The neural basis of the models provides insights into the action and reaction, response time, flow etc., and can provide information to improve the model. Use neuroimaging data to improve AI: Neuroimaging techniques, such as Functional MRI (FMRI), can yield insights into how the brain processes information. There is a stimulus in the human brain which is an event that initiates a reaction in an organ or a tissue. Understanding such stimuli further simplifies how the brain processes information. AI models and neural architecture: To enable accuracy in AI models, creating AI architecture which mimics neural architecture plays a very important role. This includes models that use spiking neurons, which are more like biological neurons than the traditional artificial neurons used in most AI models. Incorporate feedback mechanisms: Feedback mechanisms are an important aspect of the brain’s processing, and by incorporating them into AI models, you can improve their performance and make them robust. <cmsannotation type="cmsannotation" annotationid="AI use cases that are influenced by neuroscience" comment="subhead" text="AI use cases that are influenced by neuroscience">AI use cases that are influenced by neuroscience</cmsannotation> Brain-Computer Interfaces (BCIs): BCIs use ML algorithms to interpret brain signals and translate them into commands that can control external devices such as prosthetic limbs or computer systems. These systems can be used to restore mobility to individuals with paralysis or other disabilities. Natural Language Processing (NLP): NLP is the ability of computers to understand and interpret human language. NLP researchers have looked to the brain for inspiration, modelling the processes that the brain uses to process language. Vision-based AI: The human visual system is incredibly complex, with billions of neurons dedicated to processing visual information. AI researchers have been inspired by this system and have developed computer vision algorithms that simulate the processes of the human visual system. Cognitive computing: Cognitive computing is a type of AI that is designed to mimic the way that humans think and reason. This includes the ability to recognise patterns, understand language, and make decisions based on incomplete or uncertain information. In conclusion, the intersection of neuroscience and AI has the potential to lead to many exciting developments and breakthroughs in both fields. By working together, researchers can leverage insights from neuroscience to improve AI and use AI to better understand the brain. However, it is important to approach the intersection with caution and address any potential ethical concerns that may arise. Disclaimer: This article is authored by Jitesh Nair, BPM Expert &amp; Sourav Ghosh, BPM Expert. The opinions expressed are those of the author and do not necessarily reflect the views of Business Insider India.

Decoding neurons – Bridging mind and machines in the era of artificial intelligence

<ul><li>With the proliferation of the internet and IoT devices, we are generating an unprecedented amount of data every day.</li><li>In order to make such data suitable for analysis, it must be cleaned, transformed, and organised.</li><li>Data visualisation plays an important role in this process by providing data scientists and engineers with the opportunity to search for outliers, inconsistencies, or patterns in their data, thereby enabling insightful decision-making.</li></ul><a href="https://www.infosysbpm.com/offerings/functions/sourcing-procurement-outsourcing/insights/documents/generative-ai-in-procurement-who-is-ahead-of-the-curve.pdf">Artificial intelligence (AI)</a> is a transformative force in today's data-driven world, across various sectors. The tasks once thought to be the exclusive domain of humans can now be performed by AI algorithms powered by vast volumes of data. However, AI must depend heavily on data to do its job effectively and this requires a granular understanding of a database. In these cases, the use of data visualisation is an essential tool. It is an instrument that enables humans to gain a better understanding of insights, find patterns, and make intuitive decisions by creating visual representations of unprocessed data. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Understanding the data deluge</h2>Before delving into the role of data visualisation in AI, it’s crucial to grasp the sheer volume and complexity of the data involved. With the proliferation of the internet and IoT devices, we are generating an unprecedented amount of data every day. This data often comes in various formats, from structured data in databases to unstructured text, images, and sensor readings. AI algorithms thrive on this data, but humans need a way to understand and interpret it. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">The importance of data preparation</h2>Data needs to be prepared before AI models can train on it. In order to make such data suitable for analysis, it must be cleaned, transformed, and organised. Data visualisation plays an important role in this process by providing data scientists and engineers with the opportunity to search for outliers, inconsistencies, or patterns in their data. Visual representation of data makes it easier to detect anomalies and make informed decisions on how the data can be processed in a more effective way, e.g., scatter plots, histograms, or heatmaps. Once this is done you can concentrate on the essential parts of your project, automate tasks and make smarter choices by using <a href="https://www.infosysbpm.com/offerings/functions/bpm-analytics/case-studies/documents/battling-the-bots.pdf">AI tools</a> that enable data visualisation. AI is just beginning to take hold, but it has already made a huge impact on data visualisation professionals. With AI, there are fewer steps in the process of gathering and analysing data, which means that projects move more quickly than ever before. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Exploratory Data Analysis</h2>Exploratory Data Analysis (EDA) is a critical step in understanding the underlying structure of data. EDA involves visualising data through various charts, graphs, and plots to uncover insights. For AI projects, EDA helps in feature selection, which is crucial for training accurate models. Data visualisation tools assist data scientists in exploring relationships between variables, identifying correlations, and gaining a deeper understanding of the data's distribution. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Model evaluation and validation</h2>Data visualisation does not stop once the AI model is trained. It continues to play a crucial role in evaluating and validating the model's performance. Visualisation techniques like confusion matrices, ROC curves, and precision-recall curves provide a clear picture of how well the AI model is performing. By visualising model predictions and comparing them to actual outcomes, data scientists can fine-tune models and enhance their accuracy. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Interpreting AI outputs</h2>In many cases, AI models are a "black box" and therefore it’s difficult to understand why certain decisions have been made. Data visualisation can aid in interpreting AI outputs by creating visualisation tools that show the model's decision-making process. An example of this is in health care, AI diagnostic systems can create visual diagrams to explain their findings and make doctors more trusting and familiar with the advice they receive from AI. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Communicating results effectively</h2>The ultimate goal of AI is not just to create accurate models but to derive actionable insights from them. Data visualisation bridges the gap between AI experts and non-experts by presenting complex AI-generated insights in a visual and understandable format. Dashboards, infographics, and interactive visualisations allow stakeholders to make data-driven decisions without needing a deep understanding of AI algorithms. <h2 style="line-height:1.295;margin-top:2pt;margin-bottom:0pt;">Benefits of data visualisation in AI</h2> <ol><li>Improved understanding: Visualisation transforms abstract numbers into meaningful representations. It enables stakeholders to gain a deeper understanding of the data, fostering more informed decision-making.</li><li>Efficiency: With data visualisation, information can be absorbed at a glance. This saves time, as analysts and decision-makers do not need to sift through extensive spreadsheets or databases to extract insights.</li><li>Error detection: Visualisations make it easier to spot errors or outliers in data, a crucial step in data preprocessing for AI. Detecting and correcting anomalies early can enhance the accuracy of AI models.</li><li>Interactive exploration: Advanced visualisation tools allow users to interact with data, enabling them to drill down into specific details and explore data from different angles. This interactivity promotes deeper analysis.</li></ol>Data visualisation is an indispensable tool in the world of AI. It bridges the gap between raw data and actionable insights, enhancing understanding, communication, and decision-making. In the realm of AI, data is the lifeblood, and data visualisation is the lens through which we make sense of this vast and complex resource. It is the bridge that connects the power of AI algorithms to human understanding and decision-making. From data preparation to model evaluation, interpretation, and communication, data visualisation plays a pivotal role in ensuring that AI is not just a technological marvel but a practical and valuable tool in solving real-world problems. Transforming data into knowledge allows individuals and organisations to make full use of the potential of AI. Data visualisation, which guides us through a large quantity of information to ensure that we reach our target with confidence and clarity, is the compass for AI's journey. Disclaimer: This article is authored by Sourav Ghosh, BPM Expert and Jayaraju D, BPM Expert. The opinions expressed are those of the author and do not necessarily reflect the views of Business Insider India.

How data visualisation can empower AI insights

<ul><li>Debt collection has always been an obstacle-ridden course for most organisations. </li><li>The steep rise in consumer debt and a hoard of laws and regulations governing the process have made it even more challenging. </li><li>In this context, we talk about some best practices to enhance the debt recovery process.</li></ul>Debt collection is one of the most challenging tasks for businesses and organisations. They must not only effectively collect outstanding debts but also navigate a virtual minefield of regulations and compliance issues that can be difficult to understand. For example, <a href="https://www.infosysbpm.com/offerings/industries/financial-services/documents/automating-financial-spreads.pdf">financial services</a> providers, by law,must adhere to the stringent rules set by the Consumer Financial Protection Bureau (CFPB) when handling classified personal information. Those dealing with online payments or credit card transactions have additional requirements under the data security standards drawn by ombudsmen, which dictate how calls containing personal financial data should be monitored, recorded, transmitted, and stored. Failure to comply with these laws can lead to hefty fines from government regulators like CFPB, not to mention reputational damage. It’s crucial that organisations take extra care while ensuring compliance with relevant laws and adherence to proper procedures when dealing with sensitive consumer information to deliver the best results. The current economic climate poses many challenges for organisations, particularly in terms of debt collection. With the rise of consumer debt and tightening credit standards, companies are increasingly finding it difficult to collect overdue payments. Organisations that implement an effective debt collection approach will boost revenue and significantly ensure their company's financial stability and growth. Let’s explore some of the best strategies for improving debt recovery through efficient and ethical debt collection practices using an efficient call centre solution, that will help businesses recover what's rightfully theirs while maintaining a positive relationship with their customers. Communication efficiency through the omnichannel approach In today's fast-paced digital world, businesses need to adopt an omnichannel approach to communication to stay relevant and competitive. This means providing customers with a seamless experience across multiple channels such as email, social media, chatbots, phone calls, and even in-person interactions. However, choosing the right mix of communication options can be challenging for businesses as each channel has its own strengths and weaknesses depending on the type of message being conveyed or the target audience being addressed. For instance, the younger generation may prefer texting or messaging while the older may still prefer phone calls or emails. Addressing this challenge effectively requires understanding customer preferences and behaviours through artificial intelligence and feedback mechanisms such as surveys or user testing. It also involves developing personalised content tailored to specific channels while maintaining consistency across all touchpoints. Targeting customers based on various factors, including location, age group, or gender and creating personalised preferences like the right message at the right time through the right channel, has long been the holy grail of marketing. A firm’s technological capabilities, data, and resource availability play a vital role in achieving this and by implementing this approach, businesses can successfully enhance customer satisfaction levels resulting in increased loyalty and revenue growth over time. Automated dialling systems <a href="https://www.infosysbpm.com/offerings/functions/accounts-payable-on-cloud/insights/documents/accounts-payable-automation-insights-for-2023.pdf">Automated</a> dialers help agents quickly reach out to customers to collect past-due amounts more effectively than manual methods allow. This technology also facilitates faster follow-up with delinquent accounts so businesses can stay ahead of their collections efforts without having to sacrifice additional resources or personnel time. Payment plans Offering payment plans helps make collecting from consumers easier because they have the option of paying off their balance over time, instead of all at once. This helps consumers who may be struggling financially. It’s important that both the customer and the business understand at the outset what is expected throughout the duration of an agreed payment plan and are aware of any fees associated with it (if applicable). Offering payment plans helps make collecting from consumers easier while providing them with the financial assistance that they need during difficult times. However, it is necessary to establish clear communication beforehand to ensure successful repayment by consumers and protect both parties involved in the transaction process. Credit reporting Reporting negative information about consumers who fail to pay debts owed is another way businesses protect themselves against future losses while simultaneously incentivising individuals to make timely payments. This improves the overall cash flow operations and reduces delinquencies across broader portfolio base(s). Accurate documentation is essential for any business, especially in the highly regulated world of debt collection. The best debt collection call centres are equipped with cutting-edge technology that makes comprehensive record-keeping simple yet effective enough to establish accuracy at every stage of the process – from initial contact toresolution (if necessary). This includes features such as automatic logging of incoming calls; automated recording capabilities; detailed reporting functions; customisable templates for creating standard documents like letters and emails; secure storage options so everything remains organised and accessible over time, etc. All these features help streamline communication between collectors and customers while also providing real-time data about how well a team is performing overall for each account/case they work for on a day-to-day basis. Apart from utilising these strategies, organisations can adopt other best practices such as providing helpful reminders before overdue notices go out and offering online portals where people can manage account balances securely via a web browser. This enables them to be well-positioned in the competitive marketplace and capitalise on all opportunities. Centralised Data Management Systems with Analytics As businesses continue to struggle with the challenges of a volatile economy, it is more important than ever that they maximise their revenue recovery process. By centralising customer information in a single repository, debt collection teams can be better positioned to automate and personalise this process. Centralising customer information in a single repository can be a game-changer for debt collection teams. The benefits of such an approach are numerous: not only does it reduce operational costs associated with collections staff manually searching through multiple databases for account information; but also reduces time spent on resolving conflicts between customers and creditors due to incorrect or outdated data being used by the collection staff. When all customer data is stored in one place – including payment history – organisations can easily identify which customers may need extra attention during the collection process as well as those who may be eligible for special payment plans or discounts based on their past performance records. Additionally, deploying Machine Learning and <a href="https://www.infosysbpm.com/offerings/business-transformation-services/insights/documents/linear-algebra-in-ai.pdf">Artificial intelligence</a> enables companies to track customer behaviour over time, so they can better anticipate future needs. This ultimately results in improved efficiencies. As businesses continue to scale and grow, they need to find ways that allow them to maximise their ROI while excelling at their service delivery. Fortunately, there are a variety of techniques available today that can help businesses achieve this goal. When these techniques are combined into an integrated strategy, the potential opportunities become even more powerful. Disclaimer: This article is authored by Sourav Ghosh, Senior Industry Principal, Infosys BPM and Rajanna AP, Senior Lead Analyst, Infosys BPM. The opinions expressed are those of the author and do not necessarily reflect the views of Business Insider India.

Debt recovery best practices: Lessons learned from successful business experiences

<ul><li>When today’s customers think of banking, the first thing that comes to their mind is online or digital banking. </li><li>However, even though the terms online banking and digital banking are used interchangeably, they are not the same. </li><li>In this article we explore digital banking as a concept and understand the distinct traits of a truly digital bank.</li></ul>The unanticipated global pandemic caused a shift to the digital era for all kinds of businesses. Banking is no exception. In the last few years, the number of consumers visiting a bank branch for banking services has dwindled quite a bit. In this article, let’s reflect a bit on how digital banking is revolutionizing the banking industry. When one considers the digitalisation of global banking infrastructure, a terminology that comes first into mind - “online banking”. How is it different from digital banking? People sometimes use words a bit loosely without clearly understanding their meaning. This is why many people confuse digital banking and online banking. In reality, they are two separate concepts. In case of online banking, the core aspects of a bank are made available online. These include deposits, withdrawals, fund transfers, and bill payments. But for anything more than this, you have to visit a branch. Hence, online banking is a technology that goes hand-in-hand with branch-based banking. In the case of digital banking, the entire banking service is made available through the internet. You can access the digital bank through an app or its website. All operations that a consumer avails through a traditional bank branch can be availed through the app. It embraces automated processing of requests from consumers and removes the hassle of paperwork completely. In digital banking, high-level of process automation is present and includes application programming interfaces (APIs) for cross-institutional services. Financial data can be accessed through desktops, mobile phones, or ATMs. A brief history of Digital Banks Digital banking is not a brand-new technology that came into existence in the last few years. The green shoots of digital banking technology emerged in the ‘60s, with ATMs and cards that were dubbed as “e-banking” in many countries. Then in 1994, Microsoft Money, a personal finance management program, incorporated online banking. That was when thousands of households started accessing banking services online. The first digital-only banks were established in 1997 and 1998 in Canada and USA respectively. These banks were Tangerine (Canada) and First Internet Bank (USA). With the proliferation of faster internet connectivity, digital banking has become a modern-day phenomenon now. As of 2020, over 76% of consumers receive their banking services through digital banks. Distinct traits of a truly Digital Bank A digital bank is not the result of a functional website or an app. It’s much more than that. There are a few fundamental aspects that are very important for every digital bank. 1. 24x7 invisible banking: Banking is evolving rapidly today. Hence, digital banks should be able to provide anytime/anywhere experience to its consumers without needing physical presence. This essentially eliminates filling up paper forms and standing in queues. Banks have now started doing e-KYC through voice, video, and biometrics. 2. Data aggregation: Digital banks should be capable of utilizing data more effectively. Successful <a href="https://www.infosysbpm.com/blogs/digital-business-services/digitally-transforming-financial-institutions.html">digital banking</a> corporations use data to provide a more personalized experience to their consumers. This is made possible by analyzing spending patterns, repayment obligation, and other data. As an example, a bank in Slovakia is doing customer onboarding using a third-party mobile application. This makes it 70% faster than the process followed by normal banks. They use neural networks, biometrics, and machine learning for doing identity verification of customers. Authentication is done through customer device cameras and biometric technology to track eye movements, facial features, and light conditions. 3. Open banking: Open banking acts as the backbone of data aggregation. Through this, banks are now able to augment customer information through third-party vendors, like a retailer or a utilities company. 4. Security: Security is quite an important aspect in banking. New age security in the banking system is characterized by multiple layers, two-factor authentication, identity verification, facial recognition, fingerprint scanning, and voice recognition. Digital banking is clearly a reality that is continuing to disrupt the financial world. In the coming years, a combination of blockchain and P2P could cause market decentralization. Also, the big tech companies will continue to digitise the banking platforms. Moreover, challenger and neo-banks, which are small now, will grow to rule the banking market. It’s clear that digital banking is here to stay. Question is, how do we as service providers help our clients embark on that journey? Disclaimer: This article is authored by Sourav Ghosh, Senior Industry Principal, Infosys BPM. The opinions expressed are those of the author and do not necessarily reflect the views of Business Insider India.

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