Automation and AI


Welcome tothe Future

The word sentiment refers to an attitude, feeling, or emotion associated with a situation, event, or thing—an opinion—which can be difficult to quantify, even using traditional modes of opinion mining or sentiment analysis. Deep learning and inference dramatically improve sentiment analysis in two ways: Increasing accuracy Making opinion mining much more useful
The Internet of Things is having a massive impact on all kinds of companies in all kinds of industries, generating headlines about large-scale, industrial-strength applications of this powerful technology framework. But IoT—sensors, the infrastructure to capture sensor data, and the Big Data analytics required to put it to work—is finding more and more applications in consumer products.
Deep learning using neural networks and Graphics Processing Units (GPUs) is starting to surpass machine learning for image recognition and other applications. Deep neural networks are helping to advance self-driving cars, faster development of new drugs, and real-time multiple-language translation for online chats. Find out more about: Machine learning versus deep learning The power of inference GPUs delivering near real-time performance
Making conversational agents real. Ready for more user-friendly robotic customer service agents? Natural Language Processing (NLP) and Natural Language Understanding (NLU) use advanced AI techniques to analyze, contextualize, and understand human speech. NLP and NLU are improving at such a rapid rate that they can enable meaningful and normal conversations between machines and people. They make conversational agents a reality. Learn more about: Retrieval and generative NLP models Open and closed domains Deep learning and NLP
Now, with the emergence of high performance cloud-based analytics storage like Google Big Query, AWS S3 and others, BI tools are looking to leverage these as comparted to in-memory. Looker is one such example that is using the processing power of high performance data storage rather than replicating the data into the memory. RESTful web services enable interactive analysis for massively large datasets and works in conjunction with Google storage. Other systems build for high performance analytics storage are Amazon Reshift, Snowflake. Even legacy BI tools like SAP BO, Oracle has come up with their BI versions for cloud.
To cope with the disruption in their business models, banks need to manage high data volumes, including managing unstructured data, in real time. To achieve this, banks are extending their existing data warehouse with NoSQL data, and offloading computation and storage to public or private or hybrid clouds. Banks are replacing and augmenting their Extract, Transform and Load (ETL) with intelligent data ingestion and data preparation, augmented by automated data governance enabled by semantic definitions. Banks are also building linguistic computing capabilities to manage unstructured data. This blog explores some emerging technologies being embraced by banks with respect to building intelligence, managing large data volumes, managing cost and building flexible and configurable technology. Building intelligence To make engagement with digital natives intelligent, banks in today’s world are collecting and analyzing GPS, spatial and cyber logs on multiple tracks. They have also been seen to implement data lakes to manage the number crunching required to handle such huge and low latency data volumes. Customers need real time data and banks in sync with the requirement are leveraging Internet of Things (IoT) in ATMs, mobile banking and weblogs. A way ahead of all these steps to build intelligence, Machine Learning techniques are being implemented in the risk management and fraud arena for real time analysis and alert generation. A cohesive effort of these is only enhancing the customer experience quotient, a much needed factor in today’s competitive world. Managing large data volumes The volume of data generated and collected in the banking sector is enormous. But in this age of big data, are those data being utilized or applied properly? With the wider use of unstructured data, the data at banks is growing significantly every year. Existing data solutions are not geared to cope up with this massive data growth. In such scenarios, banks are looking to implement data lakes embedded with intelligence to manage the enormous volume of text, voice and video. To ensure a definitive process and allow the industry to largely benefit from this huge data clutter, new transaction processing as well as analytics architecture is now embedded with data lineage, data governance, data intelligence and reconciliation capabilities in data preparation. This helps banks to address challenges created from multi-format, multi-source, multi-definition, low latency data requirements. Yes, banks need to build predictive capabilities to manage the large data volumes, and that is why banks are investing in data science to generate better returns, propel customer engagement and adhere to compliance guidelines. The industry is on a roll. Managing cost The consistent problem with banks is to manage costs effectively. Surviving competition and rendering optimum customer value at the same time is a key challenge that banks face. In such a scenario, to garner savings and optimize costs, banks are leveraging data lakes on commodity hardware/appliance with open-source software. They are also streamlining the decision on data management by helping existing data warehouse to offload storage and computation to cloud. In a separate attempt, within cloud, they are dividing the load between archival storage vs daily use data as well. Building flexible and configurable technology Flexible data integration and data preparation capabilities leveraging big data and data lakes are helping banks to create visible and intelligent transformation. This helps in creating the traceability needed for privacy regulations like CCPA and GDPR and BCBS 239 and can also help in replacing monolithic ETLs with data streaming and API integration capabilities to support Open APIs and marketplace. The regulatory hunger for data has grown 10X in the past 10 years or so and this is clearly likely to grow by another 10X in upcoming five years. Therefore, banks are building microservices-based risk, finance and regulatory infrastructure to address the upcoming changes. They are also embedding intelligence and flexibility into their data infrastructure to make the overall operational processes seamless and convenient for customers. At the end of the day, customer is the king.
Banking technologies are undergoing a major shift as banking companies face the challenge of servicing digital native customers with real-time service requirements supporting voice and text conversations and with low data latency to support marketplace and regulatory requirements. Banks can prepare themselves to support such a paradigm shift in servicing requirements by embedding their data infrastructure with intelligence. Unless intelligence is contextualized for a bank and built into every component of the data infrastructure, the embedded intelligence does not create the intended beneficial impact. Data infrastructure can be divided into four components: data preparation and integration, data storage and data warehouse, analytics and visualization Data . This blog discusses how intelligence and analytics can be embedded into every component of data infrastructure to enable banks to reduce latency. Business intelligence is not a naïve term in the banking domain. A cohesion of smart tools and techniques create business intelligent solutions that are increasingly being adopted by banks. This makes the process seamless, convenient for customers and ensures precision in business outcomes. 1. Intelligent data preparation and integration Banks are focusing on making data preparation and integration intelligent right from data ingestion to data preparation. They are being embedded with visual exploration for generating exception, analytics and insights on integration, the end objective is to facilitate accurate results. The other function that banks are looking to implement is to enable visual based data discovery in terms of geo-spatial capabilities and create empowered business users. On the other hand, automated data governance capabilities supported by data quality, meta data, data custodian, data definition and semantic definitions, traceability, privacy, custody, and metadata are also being developed to enhance the overall offerings for customers. 2. Efficient data storage and warehouse Do banks have processes in place that ensures that the huge data available to them can be trusted? For this, data storage and data warehouse plays a key role. A storehouse of data is essential to eliminate operational challenges. It is, therefore, essential for banks to extend the logical data warehouse to no SQL data, data in other formats, offloading computation and storage to public or private or hybrid cloud. Additionally, banks should also look at having an ‘In-memory columnar engine’ for faster and better performance. That in turn will support interactive and visual applications, multiple data sources, complex data models and complex calculations. 3. Augment data analytics and visualisation In today’s data driven world, it is critical for banks to adopt tools that will help analyze, process and evaluate data to generate strategic business outcomes. Customers and businesses need clear information to operate in an intelligent manner. That is why enhancing the analytics and visualisation with linguistic computing capabilities is essential for banking processes. Banks can even adopt Natural Language Processing (NLP) and Geo spatial intelligence to augment analytics. NLP is a simple way to raise a query on data and generate narratives to explain drivers and graphs, making the entire process clear and transparent. Voice and search based interface for raising a query on data is another way to enhance the data and analytics capabilities. For a more engaging platform, banks could even weigh conversational analytics that in the form of chatbots and virtual assistants help in driving precision in the operational workflow.
Until a few years ago, mobile networks traffic had a predictable demand pattern. Hence, planned node placements or capacity assignments with network processing software capabilities tied to the particular physical node worked reasonably well. Designers could run networks near-optimally within the constraints using well-known Erlang models with given call blocking probabilities. The network state was often kept in near static simple repositories such as trees, tables etc. However, in the last few years data usage has increased significantly. Data is much harder to model than voice, with new applications such as Pokemon GO being virally popular for just a few weeks before tapering off. Network designers tried to address new demand patterns via hardware-software separation using virtualization methodologies such as NFV with similar repository architectures as before. This method somewhat worked since the traffic was still human-centric. Recently, with the increasing power of data and data-analytics, “things” have also started to get connected. These “things” range from small meters and cookers to huge wind-turbines and factories, spitting out data at rates anywhere from a few bits to multiple gigabits every second. Orthogonally, there has been a growth spurt in new applications and business models, such as ride-sharing, Industry 4.0 etc. that fundamentally assume the presence of connectivity at every time and place with reliability requirement of 5 or 6 9s. With network having to address not only human-human communication but also that of human-to-machine, machine-to-machine, machine-to-cloud and more, a new network was needed. The new 5G standard is designed with precisely these requirements in mind - ability to reliably handle the extreme variability in requirements for all traffic types. From the beginning, 5G was designed with flexibility in both radio interfaces and access and core networks. However, by itself, it was not sufficient since the same network was required to simultaneously serve increasingly diverse use cases. This led to a new cornerstone of a successful 5G network design: the concept of network slicing. ETSI has defined a network slice as a description of a service aware logical network that is composed of different physical or virtual network elements, resources and functions, often for the purpose of efficient utilization of the network while meeting the required service specificationi. Network slices are driven by service assurance requirements and are different from providing QoS to individual streams. Since the slice is end-to-end and touches almost all aspects of the network, many major cellular organizations are involved in its specifications. In particular, ETSI is working on defining frameworks required for network slice implementations, GSMA is defining information models for slicing, and 3GPP is working on provisioning and resource management of RAN and core to support the slice

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