Why Banks and Financial Institutions are using Speech Analytics
Banks and Financial institutions are rapidly expanding the channels they use to sell and serve customers and the platforms and technologies used to support these channels often vary from location to location and country to country. Therefore, there is a pressing need for robust and adaptabily solutions. Speech Analytics is one of the fastest growing technologies available and is expected to be worth $2,175.8 billion by 2022. This technology offers numerous benefits for the financial industry for a variety of reasons such as analyzing and organizing large amounts of data that is otherwise unusable.
How does Speech Analytics work?
Currently, there are three different types of technologies available on the market: phonetics-based, speech-to-text, and grammar-based. Our cloud-based call recording solution Recordia can be equipped with speech-to-text analytics, a process that converts unstructured conversations into metadata that can be analyzed later.
– Audio is processed into data.
The first step is taking recorded audio interactions and converting them into meaningful and easily searchable data. Each Speech Analytics software will vary depending on the company and the technology behind its solutions, some systems will go deeper than others, so make sure to do your research on what functions and capabilities you require. For example, Recordia is capable of transcribing speech to easily searchable text in more than 60 languages.
– Refining data
Once the conversations have been converted into data, they must be refined and prepared to obtain insights. At this step, a speech engine performs the initial analysis and converts the collected information into a series of phonemes, or small phonetic sounds. The results of this process are indexed and made searchable via a query engine.
– Data analysis
After the software has transcribed and refined the collected data, you can use it to identify and automatically analyze keywords, phrases of interest, topics discussed during the calls, and even reveal trends and opportunities. Usually, the results are presented directly on your dashboard, or through generated reports. With Recordia, you can search information among millions of calls.
Why Banks and Financial Institutions are using Speech Analytics
1. Compliance and regulations
The Finance industry is one of the most regulated with legislation such as The Dodd-Frank Act, MiFID II, MAR, CCPA and many others which are becoming even more stringent in their compliance requirements and increased fines and penalties for companies who fail to comply. These become even more complicated and difficult to navigate if you operate in multiple states and/or countries.
This ever-changing landscape of regulatory oversight continues to add scope and complexity along with increasing cost to maintain up-to-date compliance programs. In order to stay compliant with these requirements it is necessary to have a strategic long-term plan and take a preventative approach to handling regulations.
There is an increasing demand from Banks and FI’s requiring Speech and Data Analytic solutions, not only for compliance purposes, but allows access to huge amounts of unstructured data that can be used to ensure best practices, staff restraining, sentiment analysis, spotting trends and improving products/services.
2. The way clients communicate is changing
There are more ways to communicate now more than ever before, making it even more complicated for the financial industry to monitor analyze interactions, while staying compliant. To comply with regulations and industry standards companies have the complex task of managing and analyzing is monitoring the thousands of interactions firms have each day with their customers and prospects. Every single customer conversation represents the potential for a compliance breach. Recording all these channels requires a solution that, at a minimum enables the recording of all channels, that can store and encrypt said recordings with a high level of security.
3. Fraud prevention
Unfortunately, fraud is still one of the biggest threats to the financial sector and it is becoming more sophisticated, which is why there is a growing demand for tools that aid in combating this. The financial industry needs to be prepared and take a proactive approach. Finding a solution that can offer a way to better identify and prevent fraudulent activities is imperative.
With such a large emphasis on cybersecurity and fraud prevention online, cybercriminals are changing their tactics by targeting businesses via phone conversations. Speech analytics can be used for this purpose by establishing rules to identify fraud-like behaviors and key words in conversations so that steps can be taken to mitigate the risk.
How does Recordia’s Speech analytics work?
Recordia records and encrypts interactions from multiple platforms, including landline and cell phone calls, emails, fax, SMS, social media, video conferencing and even face-to-face conversations. Once recordings are stored, speech analytics and its functionalities can be applied.
– Word Spotting
Identify and categorize calls according to defined keywords. For example, identify and tag all calls in which the word “unsubscribe” or phrase “cancel the service” is used, as well as calls that mention competitor names or that use expletive words.
Recordia’s Speech Analytics feature has the ability to start a workflow from the moment a call is tagged. For example, under CCPA Californians have the right to access and delete any personal information a bank or FI holds. With Recordia all calls that with the phrase “remove personal information” could be tagged and start a workflow so that those calls go directly through an email to a compliance officer, so they can start the process for deletion.
– Sentiment Analysis
Detect the polarity in the audio of a call with a combination of attributes such as tone of voice, speakers overlapping or any other additional behavior that occurs during a conversation. With Sentiment Analysis, the accuracy of polarity in the analysis increases and multiple variables can be generated to analyze, for example, positive, neutral, negative
Sentiment Analysis detects emotions, such as happiness, frustration, anger, sadness, etc. Many emotion detection systems use lexicons, that is, keyword lists, or complex machine learning algorithms for the analysis of voice tones and other characteristics.
– Automatic work-flow
Once keywords and groupings have been identified anytime a word or phrase under these categories is mentioned, they are tagged, and a workflow is automatically initiated. Alerting the relevant department or supervisor of a request or issue. You can set up alerts for any compliance breaches and customize the solution for your unique compliance requirements