Suggestion confidence today

INF-67 · Multiple tiers of suggestion · how the ledger matcher scores its suggestions, and what auto-maps

0 – 100%One number per suggestion. Not a range.
2,040first suggestions replayed against 10 companies' Miracle books
80.9%of them were the ledger the books used
Noneconfidence threshold for auto-mapping

1. What the score is

2. What auto-maps, and what only suggests

Saved automatically (rules, no score)

  1. Contra pair: two own banks, same UTR/RRN, equal amounts (or a fee ≤ ₹50 and 1%), ≤ 3 days apart.
  2. Exact match: every sure exact matcher names the same ledger:
    • UPI ID mapped before
    • account or phone mapped before
    • narration pattern mapped before
    • ledger's full name in the narration
  3. Already in Miracle: another bank's posting in the books is booked against this bank, and nothing else matches.

Only suggested (has a score)

  • Every other clue: similar narration, category, recurring, similar name, name cut short or spelled differently, one name word, and exact matchers that are not sure.
  • The first suggestion is filled in. The user must accept it (Enter twice, or Ctrl+Enter).
  • There is no lower limit. A 40% limit was removed because full names in narrations fell below it.

3. Where the score shows today, and the decision

4. Every situation and its score

≥ 90%70–89%< 70%Shown = first suggestions in the replay · Right = the books used that ledger
SituationShownRightConfidence
Narration pattern mapped before
every earlier choice agrees
1111
100%
Narration pattern mapped before
every earlier choice agreesonly candidate
4848
100%
Ledger name, cut short, in narration
most words matchonly candidate
1414
99%
Ledger name, cut short, in narration
part matches
1010
99%
Ledger's full name in narration
6262
99%
Ledger name, cut short, in narration
full matchonly candidate
77
98%
Narration like an earlier mapped one
full matchonly candidate
335324
97%
Narration like an earlier mapped one
most words matchonly candidate
55
97%
Ledger name, cut short, in narration
part matchesonly candidate
10196
95%
Narration like an earlier mapped one
part matchesonly candidate
6057
95%
Narration like an earlier mapped one
full match
8479
94%
Recurring payment
long historyonly candidate
1111
93%
Account / phone mapped before
mapped the other way before
22
91%
Category (charges, interest, tax…)
category has 2 ledgers
1111
91%
Account / phone mapped before
mapped the other way beforeonly candidate
11
90%
Recurring payment
only candidate
66
90%
Ledger name, spelled differently
full matchonly candidate
2018
90%
Ledger name, spelled differently
part matchesonly candidate
1715
88%
Name like a ledger name
full matchonly candidate
240199
83%
Category (charges, interest, tax…)
category has 2 ledgersused this way before
33
82%
Category (charges, interest, tax…)
category has 3+ ledgers
157126
80%
Ledger's full name in narration
only candidate
129103
80%
Name like a ledger name
full match
11
78%
UPI ID mapped before
every earlier choice agrees
54
78%
UPI ID mapped before
most earlier choices agree
97
77%
Category (charges, interest, tax…)
category has 3+ ledgersused this way before
86
74%
UPI ID mapped before
every earlier choice agreesonly candidate
107
72%
Narration like an earlier mapped one
most words match
31
71%
Category (charges, interest, tax…)
category has 1 ledgeronly candidate
388267
69%
Recurring payment
long history
10
66%
Category (charges, interest, tax…)
category has 1 ledger
148
61%
Name like a ledger name
part matchesonly candidate
14387
61%
Narration like an earlier mapped one
part matches
2211
58%
Name like a ledger name
most words match
52
57%
Category (charges, interest, tax…)
category has 1 ledgerused this way beforeonly candidate
3720
56%
One word of a ledger name
word written in full
159
56%
Name like a ledger name
part matches
20
53%
Category (charges, interest, tax…)
category has 1 ledgerused this way before
20
51%
Recurring payment
51
49%
One word of a ledger name
word written in fullonly candidate
3011
38%
One word of a ledger name
word spelled differently
60
20%

5. Tier options

How the replayed suggestions fall into three tiers with different cut-offs. Each tier cell shows how many suggestions it holds, then how often they were right.

Cut-offsHighrightMediumrightLowright
High ≥ 90% · Medium 70–89% · Low < 70% suggested78839% of all96.7%58229% of all81.1%67033% of all62.1%
High ≥ 95% · Medium 75–94% · Low < 75%65332% of all97.1%69634% of all84.2%69134% of all62.2%
High ≥ 85% · Medium 60–84% · Low < 60%80539% of all96.5%111154% of all73.7%1246% of all43.5%

6. How the row could look

₹12,400Sharma TradersNarration like an earlier mapped oneHigh · 97%
₹1,180Bank ChargesCategory (charges) · 3+ ledgersMedium · 80%
₹5,000Ravi KumarOne word of a ledger nameLow · 38%

7. Decisions for you

  1. Cut-offs. Suggested: High ≥ 90%, Medium 70–89%, Low < 70%. High is then right 96.7% of the time.
  2. Auto-accept High? Today nothing with a score saves by itself. Auto-accepting High would save about 39% of suggestions, and about 1 in 30 of them would be wrong.
  3. Suggestions step. Group or filter by tier, and let the user accept a whole tier at once?
  4. Badge text. Show the tier only, or the tier and the %?