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Big data and AI: 3 real-world use cases
Big data and artificial intelligence work together to help companies improve customer experiences in new ways. Consider these big data and AI use case examples - from invoice processing to mining phone call recordings
“Fintech, or financial technology, illustrates perfectly how AI/ML is shifting how banking institutions provide financial services to consumers today,” says Sameer Dixit, general manager of data, analytics, and AI/ML at Persistent Systems. “Back-office operations at banks involve large and complex data sets that are labor-intensive. When handled by robotic process automation [combined with] AI/ML, there’s significant savings on time and costs when performing tasks such as ‘know your customer’ checks, where the identity and address of the customer is verified. The loan process itself is also labor-intensive. With AI/ML, the ability to reduce costs and offer loans at more attractive rates to those with limited credit history is widening a previously underserved market.”
[ Sort out the jargon jumble. Read: AI vs. machine learning: What’s the difference? ]
"Artificial intelligence is improving data analysis in the mortgage industry right now in a number of ways,” Jagannath says, pointing to three areas as examples of where it can deliver benefits to lenders and customers alike:
- Throughput: “The industry average to close a mortgage right now is about three to four weeks. Using AI to automate ‘critical path processes,’ you can get mortgage processing down to just a matter of days,” Jagannath says. “This increase in throughput makes the home-buying experience faster and less stressful for the home buyer and helps banks and other lenders process more loans faster.”
- Speed of analysis: Loan processing is, in a sense, another way of saying information processing. And with mortgages, there’s a whole lot of it. AI can speed this up, to the point of real-time processing. “AI is increasingly being used at the point of sale to provide more borrower self-service,” Jagannath says.
- Accuracy in processing and outcomes: “Using AI and automation, you can process mortgages with high accuracy rates,” Jagannath says. “Humans can get tired, and that fatigue can lead to mistakes, while AI can work on a 24-7 basis with no fatigue and high accuracy."
Of course, financial, healthcare, and other companies are going to have to fight AI bias as they cut that red tape.
[ How can you ferret out bias? Read also: AI bias: 9 questions leaders should ask. ]
3. Making better use of video and voice assets
When you think of media formats that can produce inherently “big” data in various organizations, voice and video typically come to mind. Both provide examples of how AI can be applied to improve how companies manage and derive value from existing media assets, or to improve how they use these and other formats going forward.
Brian Atkiss, director of advanced analytics at Anexinet, notes that AI disciplines like NLP create considerable new improvements in how organizations use their voice data, from speech analytics to voice-to-text transcription.
Moreover, AI can solve the challenges associated with the underlying data, such as huge storage impacts. Every time you’ve ever heard “this call may be recorded for quality assurance and training purposes” is, in effect, making big data bigger.
“Previously companies would store call recording data for manual review and compliance reasons, in some cases for seven years or even longer. This data was recorded in mono (single-channel) and heavily compressed to reduce file sizes and storage costs,” Atkiss explains. “With the advances of speech-to-text algorithms, this call recording data has suddenly become a treasure trove of useful data that companies can utilize for measuring customer experience and improving operational performance.”
The AI-driven opportunity for new analytics also reinvented the storage challenge associated with call recordings and other voice data. Those mono recordings won’t cut it.
“Higher-quality audio files produce much better accuracy from speech-to-text algorithms, so companies need to use uncompressed audio, which can be more expensive to store,” Atkiss explains. Enter AI again, this time for its ability to transcribe voice recordings automatically.
“These recordings can now be transcribed in real-time or near real-time, and the resulting transcripts provide a record of the call and can be used for advanced analytics,” Atkiss says. “These text transcripts can be stored while the high-quality uncompressed audio files are able to now be deleted and don’t need to be stored. The ability of companies to provide real-time access to this data has also required advances in how the data is stored and processed.”
Video can present similar opportunities and challenges. AI is now enabling businesses to better manage and find value in their enterprise video assets.
“AI technology empowers businesses to understand and optimize video content libraries with advanced metadata enrichment and previously untapped insights,” said Chris Zaloumis, senior director of enterprise video offerings, IBM Watson Media. “From heightening engagement and increasing discoverability to automating closed captions and furthering inclusivity, AI arms businesses with the tools necessary to operate in a truly global, always-on environment.”
Speech-to-text technologies can be a huge help in terms of improving the accessibility and inclusivity of video applications, including in live feeds: “Practical applications like AI-powered live and on-demand automated captioning help bridge the communication gap for ESL employees and members of the deaf and hard of hearing community,” Zaloumis says.
[ Want a quick-scan primer on 10 key artificial intelligence terms for IT and business leaders? Get our Cheat sheet: AI glossary. ]