Banking and financial institutions are heavily dependant on manually processes. From a first-time customer trying to open an amount to applying for a loan, the process requires multiple forms to be filled, various documents to be submitted and validated. This leads to time consuming cumbersome processes, which delays completion of transactions, thereby hindering business. Automation is therefore the focus across the global banking industry.
Consider a bank that operates in multiple countries, across multiple continents. The bank has multiple verticals such as corporate banking, retail banking, personal banking, investments, payments and so on. All of these sectors are strictly regulated—with the banks facing heavy penalties if they fail to comply. If the regulations change, it often entails that they must be manually reviewed and changed—which might result in errors.
Financial data generated across verticals is stored in legacy systems and lacks standardization. A branch of the bank in the US and in India might use different systems to record and store data.
This data is usually textual, ranging from transaction data, balance sheets, assets to regulatory information, country specific financial information and so on.
The available data is not just vast, but also extremely complex and interrelated. Processing, analysing and interpreting data in its raw format is time consuming, but also requires a considerable amount of human intervention. Even if the manual data management is done in an extremely structured and carefully crafted environment, there is still a huge scope of error.
See the common theme in all the use cases above? Manual involvement is cumbersome, time consuming and is often error prone.
In this context, banks are looking to deploy and leverage the latest technologies to increase productivity, optimise cost and resources, improve customer experiences, and make better and much more informed decisions.
What’s the solution?
Companies need a comprehensive framework and not a single tool that replaces existing technology or human intelligence. A combination of tools including robotic process automation, AI, business management software would result in a sophisticated automation framework.
Hyper automation brings together capabilities such as machine learning, process mining, RPA, API integration and AI to replace high levels of complexity with automation. Hyper automation is a framework that streamlines the tasks involved within a process by automating data validation, integration making it quicker, more consistent, and less prone to error. This not only speeds up digital processes but reduces human intervention, thereby reducing operating costs.
A few key areas where hyper automation helps:
- Processing data collected across disparate sources. Emails, chats, text documents, excel files- both structured and unstructured data- can be processed optimally.
- Automating business processes and workflows, which results in optimizing employee time, productivity, and efficacy
- Informed decision making with predictive analytics and leveraging AI and ML technologies.
Hyper automation for financial fraud detection
Payment fraud is a pain point for most financial companies. It can be in the form of credit card fraud, insurance fraud, e-commerce fraud, and even more complex frauds.
Detecting and reconciling financial fraud is often an expensive and time-consuming process with a huge number of stakeholders involved. As there are hundreds of fraudulent transactions reported each day, manually identifying, validating, and processing them is a very slow process. Moreover, it results in a poor customer experience and loss of trust.
Most companies would rather invest in pre-emptively identifying and preventing fraud rather than receive a fraud complaint. Knowledge graphs can be leveraged to detect and stop frauds from occurring.
Using hyper automation, we can automate the following workflows that are the basic steps in the fraud detection process
- Extract and Integrate the siloed data across various data sources—internal databases, external sources such as known financial criminal databases.
- Identify if there is an abnormality in a customer’s transaction based on historical data
- Notify the security/compliance team with an alert for further investigation and due diligence to determine if the alert is genuine.
- Send a notification to the customer in the form of an SMS, email, or through a preferred communication channel.
Intelligent AI solutions can be used to monitor all transactions and check for fraudulent activities round the clock. Predictive models can be leveraged to predict the possibility of fraudulent transactions will be an added advantage. The hyper automation technology once implemented, can be used for prediction and prevention of errors and loss of business.
Why Corporate Memory?
eccenca is a leading vendor of knowledge graph enabled hyper automation solutions. eccenca Corporate Memory, transforms background knowledge about products, process, partner, people, policies, and data into understandable and executable containers.
Corporate Memory visualizes information and relations in the form of a network. Additionally, it infuses relevant background information to understand the relations, trace the lineage of the data. This way, fraudulent processes such as multiple use of credit cards, emails and devices, ghost broking, identity and account theft or artificial identities can be identified systematically and quickly.
An efficient knowledge graph platform, Corporate Memory provides the highest degree of scalable automation in creating, maintaining and sustaining a knowledge graph-powered data fabric across dozens hundreds or even thousands of data sets as well as data reasoning.
By powering hyper automation, Corporate Memory increases employee productivity by automating tedious and time consuming processes, allowing them to focus on other tasks that can increase innovation and the organization’s profitability.
Create the ideal knowledge engine to unlock your organization’s digital transformation with eccenca!