Financial services

Biometrics to Enhance Data Security

Facing a world where account holders may need to access their finances or make payments through a wider range of services, banks will need enhanced ways of confirming their users’ identity before they permit access to a user’s financial information or approve a payment. New systems based on biometric technology can offer financial institutions and their customers methods of identity verification that are straightforward, individualised, and resilient to counterfeiting efforts. Facial recognition technologies, based on computer vision and machine learning technologies, can quickly and reliably identify specific individuals, and could allow users to unlock their accounts or approve payments without having to rely on text-based passwords that can be lost or stolen.


Virtual Assistants for Retail Banking

AI-powered assistants, also known as Chatbots, will create opportunities for banks to provide support to their customers virtually. These virtual assistants can help solve customers’ problems through a new, responsive, and always-available path to support which avoids lengthy wait times on calls to customer service hotlines or visits to bank branches. Additionally, by freeing bank employees from dealing with more routine customer service tasks, the implementation of virtual assistants may allow bank employees to re-focus their efforts on work that is more valuable and difficult-to-automate.


The Opportunities Created by Deep Analysis of Account Holder Data

As most e-shopping platforms have already demonstrated, a customer’s purchase history can become a powerful resource when processed using technologies related to machine learning and big data. The insights gained from this kind of information can be used to inform advertising campaigns targeting specific customers with new offers, or be leveraged when planning new products and services to better meet their customers’ needs. Though most retail banks possess mountains of valuable information on account holders, many still fail to effectively use this information. Problems often stem from the outdated IT infrastructure frequently found in major banks. However, a variety of new digital tools—including databases capable of handling big data and machine learning tools—can help banks make up for lost time and capture the benefits of the information they store.


Cybersecurity Strategies for Retail Banking

While quantum computing will over the long-term create distinct challenges for the cryptographic systems that financial institutions rely on, retail banks with online services are already operating in a hostile environment, fending off increasingly sophisticated attacks on a daily basis. As attacks are becoming ever more sophisticated, involving new vectors such as smartphones and web application service extensions, retail banks must invest in the latest developments in cybersecurity to protect their account holders. In particular, new developments can help them mitigate the impact of unsuccessful intrusion attempts or attacks that merely seek to degrade the performance of their online services. Systems based on machine learning may also help detect suspicious activity and notify their security teams, enabling them to investigate and respond to potential intrusions or attacks more quickly.

Understand how CamIn can help you grasp these opportunities through our expert consulting model.


professional services

The Power of Predictive Analytics in Professional Services

The professional services industry is poised to gain a variety of new capabilities and increased levels of efficiency through digitalisation. Technologies such as machine learning, predictive analytics, and natural language processing have the potential to improve the provision of professional services. Machine learning can be used to quickly develop highly accurate predictive models when working with large amounts of data, often discovering insights in high dimensional relationships that would be difficult to identify or model with conventional statistical techniques. Natural language processing can also create opportunities to analyse and summarise large amounts of text or documents efficiently. Document analysis can help researchers convert unstructured text into structured data suitable for further analysis with machine learning, while automatic summarisation systems are capable of quickly capturing the most significant themes from a lengthy document or set of documents.


Robotic Process Automation in the Financial Services Industry

Robotic process automation is a set of solutions that utilises a variety of technologies including natural language processing, machine learning, and artificial intelligence to automate tasks. These kinds of solutions have the potential to dramatically change the way common, repetitive work is completed within professional services institutions. For instance, natural language processing can be used to transform unstructured text into a more structured data format, suitable for further analysis, or to automatically summarise documents. Such systems can improve an organisation’s throughput on mundane activities like the review of documents. Additionally, as they have the potential to efficiently analyse a document or set of documents in great detail, they may be capable of identifying subtle issues that human employees could miss.

Understand how CamIn can help you grasp these opportunities through our expert consulting model.

featured case study

Professional & Financial Services: Professional Services

Updating and expanding service offering of financial advisory practice through competitive AI technologies

CamIn recently supported a multinational management consulting firm by identifying low-hanging fruit to quickly improve their current service offering and game-changing technologies to expand into new lucrative technology-enabled business opportunities. The work specifically focused on identifying technologies in artificial intelligence (machine learning, processing mining, computer vision, natural language processing), big data collation, predictive/cognitive analytics, complex data architectures. This helped them gain further access to US$100 billion worth of financial advisory service opportunities that are predicted to be available by 2030...

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Digital disruption of old-fashioned banking processes

The volume of data that is available to companies involved in professional & banking services is growing rapidly, from a variety of contributing trends.  These include substantial increases in the volume of digital payments and transactions, the availability of geospatial data from their customers’ phones and other devices, from social media, images, and AI-transcribed speech. To make use of these data sources and gain an accurate portrait of each customer, this data must be integrated, merged, and analysed.  New database software and analytic tools can unlock the full potential of this data to generate value for the business and improve the company’s market share.  Forward-looking companies are embracing artificial intelligence and pursuing initiatives to improve data quality and reduce the preparation work required to analyse their data using sophisticated predictive analytics and deliver valuable services.

Traditionally, many financial processes created a “paper trail” of physical documents. These processes were cumbersome, usually executed in person, and made it expensive to store, transmit, and maintain information. The modern financial institution is migrating to fully digital processes, which can provide significant benefits in terms of efficiency, cost savings, and improvements in security. Processes such as opening new accounts, applying for services such as loans, making payments, monitoring accounts – essentially all financial services — can be automated. While this process of digitalisation represents a vast undertaking, the first companies to adopt these new approaches are gaining substantial competitive advantages.

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